Appendix F

CENRAP Technical Support Document
 International Corporation Air Sciences

Draft Report

Technical Support Document for CENRAP Emissions 

and Air Quality Modeling to Support Regional Haze 

State Implementation Plans

Prepared for: 

Central Regional Air Planning Association 

10005 S. Pennsylvania, Suite C 

Oklahoma City, OK 73159 

Prepared by: 

ENVIRON International Corporation 

101 Rowland Way, Suite 220 

Novato, CA 94945 

and 

University of California at Riverside 

College of Engineering Center for 

Environmental Research and Technology 

Riverside, Ca 92517 

September 12, 2007

 101 Rowland Way, Suite 220, Novato, CA 94945 415.899.0700 
September 2007 

i 

TABLE OF CONTENTS 

Page 

1.0 INTRODUCTION................................................................................................................ 1-1 

1.1 Background................................................................................................................ 1-1 

1.2 CENRAP Organizational Structure and Work Groups.............................................. 1-3 

1.3 Overview of 2002 Annual Emissions and Air Quality Modeling Approach............. 1-4 

 1.3.1 Modeling Protocol ............................................................................................... 1-4 

 1.3.2 Quality Assurance Project Plan (QAPP).............................................................. 1-5 

 1.3.3 Model Selection ................................................................................................... 1-6 

 1.3.3.1 MM5 Meteorological Model Configuration for 

 CENRAP Annual Modeling ................................................................................ 1-6 

 1.3.3.2 SMOKE Emissions Model Configuration for 

 CENRAP Annual Modeling ................................................................................ 1-7 

 1.3.3.3 CMAQ Air Quality Model Configuration for 

 CENRAP Annual Modeling ................................................................................ 1-7 

 1.3.3.4 CAMx Air Quality Model Configuration for 

 CENRAP Annual Modeling ................................................................................ 1-8 

 1.3.4 Modeling Domains............................................................................................. 1-13 

 1.3.5 Vertical Structure of Modeling Domain ............................................................ 1-13 

 1.3.6 2002 Calendar Year Selection ........................................................................... 1-15 

 1.3.7 Initial Concentrations and Boundary Conditions............................................... 1-16 

 1.3.8 Emissions Input Preparation .............................................................................. 1-17 

 1.3.9 Meteorological Input Preparation ...................................................................... 1-17 

 1.3.10 Photolysis Rates Model Inputs........................................................................... 1-19 

 1.3.11 Air Quality Input Preparation ............................................................................ 1-20 

 1.3.12 2002 Base Case Modeling and Model Performance Evaluation ....................... 1-21 

 1.3.13 2018 Modeling and Visibility Projections ......................................................... 1-21 

 1.3.14 Additional Supporting Analysis......................................................................... 1-24 

1.4 Organization of the Report....................................................................................... 1-25 

2.0 EMISSIONS MODELING.................................................................................................. 2-1 

2.1 Emissions Modeling Overview.................................................................................. 2-1 

 2.1.1 SMOKE Emissions Modeling System Background ............................................ 2-1 

 2.1.2 SMOKE Scripts ................................................................................................... 2-3 

 2.1.3 SMOKE Directory Structures.............................................................................. 2-5 

 2.1.4 SMOKE Configuration ........................................................................................ 2-5 

 2.1.5 SMOKE Processing Categories ........................................................................... 2-7 

 2.1.6 2002 and 2018 Data Sources.............................................................................. 2-10 

 2.1.7 Temporal Allocation .......................................................................................... 2-12 

 2.1.8 Spatial Allocation............................................................................................... 2-13 

2.2 Stationary Point Source Emissions .......................................................................... 2-14 

 2.2.1 Data Sources ...................................................................................................... 2-15 

 2.2.2 Emissions Processing......................................................................................... 2-16 
September 2007 

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 2.2.3 Uncertainties and Recommendations................................................................. 2-17 

2.3 Stationary Area Sources........................................................................................... 2-17 

 2.3.1 Data Sources ...................................................................................................... 2-17 

 2.3.2 Emissions Processing......................................................................................... 2-20 

 2.3.3 Uncertainties and Recommendations................................................................. 2-20 

2.4 On-Road Mobile Sources......................................................................................... 2-21 

 2.4.1 Data Sources ...................................................................................................... 2-21 

 2.4.2 Emissions Processing......................................................................................... 2-22 

 2.4.3 Uncertainties and Recommendations................................................................. 2-22 

2.5 Non-Road Mobile Sources....................................................................................... 2-23 

 2.5.1 Data Sources ...................................................................................................... 2-23 

 2.5.2 Emissions Processing......................................................................................... 2-25 

 2.5.3 Uncertainties and Recommendations................................................................. 2-26 

2.6 Biogenic Sources ..................................................................................................... 2-26 

 2.6.1 Data Sources ...................................................................................................... 2-26 

 2.6.2 Emissions Processing......................................................................................... 2-26 

 2.6.3 Uncertainties and Recommendations................................................................. 2-27 

2.7 Fire Emissions.......................................................................................................... 2-27 

 2.7.1 Data Sources ...................................................................................................... 2-27 

 2.7.2 Emissions Processing......................................................................................... 2-28 

 2.7.3 Uncertainties and Recommendations................................................................. 2-28 

2.8 Dust Emissions......................................................................................................... 2-29 

 2.8.1 Data Sources ...................................................................................................... 2-29 

 2.8.2 Emissions Processing......................................................................................... 2-29 

 2.8.3 Uncertainties and Recommendations................................................................. 2-30 

2.9 Ammonia Emissions ................................................................................................ 2-30 

 2.9.1 Data Sources ...................................................................................................... 2-31 

 2.9.2 Emissions Processing......................................................................................... 2-31 

 2.9.3 Uncertainties and Recommendations................................................................. 2-32 

2.10 Oil and Gas Emissions............................................................................................. 2-32 

 2.10.1 Data Sources ...................................................................................................... 2-32 

 2.10.2 Emissions Processing......................................................................................... 2-32 

 2.10.3 Uncertainties and Recommendations................................................................. 2-32 

2.11 MMS Off-shore Gulf of Mexico Emissions ............................................................ 2-33 

 2.11.1 Data Sources ...................................................................................................... 2-33 

 2.11.2 Uncertainties and Recommendations................................................................. 2-34 

2.12 Off-shore Shipping Emissions ................................................................................. 2-34 

 2.12.1 Data Sources ...................................................................................................... 2-34 

 2.12.2 Emissions Processing......................................................................................... 2-34 

 2.12.3 Uncertainties and Recommendations................................................................. 2-35 

2.13 2018 Growth and Control ........................................................................................ 2-35 

 2.13.1 Data Sources ...................................................................................................... 2-35 

 2.13.2 Emissions Processing......................................................................................... 2-36 

 2.13.3 Uncertainties and Recommendations................................................................. 2-36 

2.14 2018 Base G C1 Control Sensitivity........................................................................ 2-37 

 2.14.1 Data Sources ...................................................................................................... 2-37 

 2.14.2 Emissions Processing......................................................................................... 2-37 

 2.14.3 Uncertainties and Recommendations................................................................. 2-37 
September 2007 

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2.15 Emissions Summaries .............................................................................................. 2-38 

3.0 MODEL PERFORMANCE EVALUATION.................................................................... 3-1 

3.1 Evaluation Methodology............................................................................................ 3-1 

3.2 Operational Model Evaluation Approach .................................................................. 3-2 

3.3 Model Performance Goals and Criteria ..................................................................... 3-2 

3.4 Key Measures of Model Performance ....................................................................... 3-2 

3.5 Operational Model Performance Evaluation ............................................................. 3-4 

 3.5.1 Sulfate (SO4) Model Performance....................................................................... 3-4 

 3.5.2 Nitrate (NO3) Model Performance ...................................................................... 3-4 

 3.5.3 Organic Matter Carbon (OMC) Model Performance........................................... 3-5 

 3.5.4 Elemental Carbon (EC) Model Performance....................................................... 3-9 

 3.5.5 Other PM2.5 (Soil) Model Performance ............................................................... 3-9 

 3.5.6 Coarse Mass (CM) Model Performance .............................................................. 3-9 

3.6 Diagnostic Model Performance Evaluation............................................................. 3-13 

 3.6.1 Diagnostic Model Performance in January 2002............................................... 3-13 

 3.6.2 Diagnostic Model Performance In April............................................................ 3-16 

 3.6.3 Diagnostic Model Performance In July ............................................................. 3-16 

 3.6.4 Diagnostic Model Performance In October ....................................................... 3-17 

3.7 Performance at CENRAP Class I Areas for the Worst and Best 20 Percent Days.. 3-17 

 3.7.1 Caney Creek (CACR) Arkansas ........................................................................ 3-17 

 3.7.2 Upper Buffalo (UPBU) Arkansas ...................................................................... 3-19 

 3.7.3 Breton Island (BRET), Louisiana ...................................................................... 3-20 

 3.7.4 Boundary Waters (BOWA), Minnesota............................................................. 3-21 

 3.7.5 Voyageurs (VOYA) Minnesota ......................................................................... 3-22 

 3.7.6 Hercules Glade (HEGL) Missouri ..................................................................... 3-23 

 3.7.7 Mingo (MING) Missouri ................................................................................... 3-24 

 3.7.8 Wichita Mountains (WIMO), Oklahoma........................................................... 3-25 

 3.7.9 Big Bend (BIBE) Texas..................................................................................... 3-26 

 3.7.10 Guadalupe Mountains (GUMO) Texas.............................................................. 3-27 

3.8 Model Performance Evaluation Conclusions........................................................... 3-28 

4.0 VISIBILITY PROJECTIONS............................................................................................ 4-1 

4.1 Guidance for Visibility Projections ........................................................................... 4-1 

4.2 Calculation of Visibility and 2018 URP Point from IMPROVE Measurements....... 4-2 

 4.2.1 Calculation of Visibility from IMPROVE PM Measurements............................ 4-3 

 4.2.1.1 Original and New IMPROVE Equations...................................................... 4-5 

 4.2.1.1.1 Original IMPROVE Equation.............................................................. 4-5 

 4.2.1.1.2 New IMPROVE Equation.................................................................... 4-6 

 4.2.1.1.3 Justification for Using the New IMPROVE Equation......................... 4-7 

 4.2.2 Calculation of the Baseline Conditions................................................................ 4-8 

 4.2.3 Data Filling for Sites with Insufficient Valid Data to 

 Calculate Baseline Conditions ......................................................................... 4-8 

 4.2.4 Natural Conditions............................................................................................... 4-8 

 4.2.5 2018 URP Point ................................................................................................... 4-9 

4.3 EPA Default Approach to Visibility Projections....................................................... 4-9 
September 2007 

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 4.3.1 Mapping of Modeling Results to the IMPROVE Measurements ...................... 4-10 

 4.3.2 Using Modeling Results to Project Changes in Visibility ................................. 4-11 

4.4 EPA Default 2018 Visibility at CENRAP and Nearby 

 Class I areas and Comparisons to 2018 URP Goals .......................................... 4-12 

 4.4.1 Example 2018 Base G Visibility Projections for Caney Creek, Arkansas ........ 4-12 

 4.4.1.1 EPA Default 2018 Visibility Projections.................................................... 4-12 

 4.4.2 Summary 2018 Visibility Projections Across Class I Areas ............................. 4-16 

4.5 2018 Visibility Projections for Base G C1 Control Scenario .................................. 4-20 

5.0 ADDITIONAL SUPPORTING ANALYSIS ..................................................................... 5-1 

5.1 Comparison of CENRAP 2018 Visibility Projections with Other Groups................ 5-1 

 5.1.1 Comparison of CENRAP, VISTAS, MRPO and 

 WRAP Visibility Projections........................................................................... 5-1 

5.2 Extinction and PM Species Specific Visibility Projections 

 and Comparisons to 2018 URP Point .................................................................. 5-5 

 5.2.1 Total Extinction Glidepaths ................................................................................. 5-5 

 5.2.2 PM Species specific Glidepaths........................................................................... 5-7 

5.3 Alternative 2018 Visibility Projection Software...................................................... 5-11 

5.4 PM Source Apportionment Modeling...................................................................... 5-12 

 5.4.1 Definition of CENRAP 2002 and 2018 PM Source Apportionment Modeling 5-12 

 5.4.2 CENRAP PSAT Visualization Tool .................................................................. 5-14 

 5.4.3 Source Contributions to Visibility Impairment at Class I Areas ....................... 5-15 

 5.4.3.1 Caney Creek (CACR) Arkansas ................................................................. 5-16 

 5.4.3.2 Upper Buffalo (UPBU) Arkansas ............................................................... 5-17 

 5.4.3.3 Breton Island (BRET) Missouri.................................................................. 5-18 

 5.4.3.4 Boundary Waters (BOWA) Minnesota....................................................... 5-18 

 5.4.3.5 Voyageurs (VOYA) Minnesota .................................................................. 5-18 

 5.4.3.6 Hercules Glade (HEGL) Missouri .............................................................. 5-18 

 5.4.3.7 Mingo (MING) Missouri ............................................................................ 5-19 

 5.4.3.8 Wichita Mountains (WIMO) Oklahoma..................................................... 5-19 

 5.4.3.9 Big Bend (BIBE) Texas.............................................................................. 5-19 

 5.4.3.10 Guadalupe Mountains (GUMO) Texas..................................................... 5-19 

 5.5 Alternative Visibility Projection Procedures ........................................................... 5-20 

 5.5.1 Treatment of Coarse Mass and Soil................................................................... 5-20 

 5.6 Alternative Model .................................................................................................... 5-21

 5.7 Effects of International Transport on 2018 Visibility Projections........................... 5-22 

 5.7.1 Elimination of International Contributions to 2018 Visibility Projections........ 5-23 

 5.7.1.1 EPRI’s Analysis of Effects of International Contributions......................... 5-24 

 5.7.1.2 CENRAP Results From Elimination International Transport ..................... 5-27 

 5.7.2 Glidepaths Based on Controllable Extinction.................................................... 5-29 

 5.8 Use of Original IMPROVE Equation ...................................................................... 5-32 

 5.9 Visibility Trends ...................................................................................................... 5-33 

6.0 REFERENCES............................................................................................................... 6-1 
September 2007 

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APPENDICES 

Appendix A: Model Performance Evaluation of the 2002 36 km 

MM5 Meteorological Model Simulation used in the 

CENRAP Modeling and Comparison to VISTAS Final 

2002 36 km MM5 and WRAP Interim 

 2002 36 km MM5 Simulations 

Appendix B: File Names, Data Source and Type and Description of Emissions 

 Used in the 2002 Typical and 2018 Base G Emissions Inventories 

Appendix C: Model Performance Evaluation for the 

CMAQ 2002 Base F Base Case Simulation in the 

 CENRAP Region 

Appendix D: 2018 Visibility Projections for CENRAP Class I Areas Using 

2002 Typical and 2018 Base Case Base G Emission Scenario 

CMAQ Results and EPA Default Projection Method and 

 Comparison with 2018 Uniform Rate of Progress (URP) Glidepaths 

Appendix E: CAMx PM Source Apportionment Technology (PSAT) 

Extinction (Mm-1) Contributions for the 2002 Worst and Best 

 20 Percent Days at CENRAP Class I Areas 

Appendix F: Extinction and PM Species-Specific 2018 Visibility Projections and 

 Comparisons with 2018 URP Points 

TABLES 

Table 1-1. Federal Mandated Class I Areas in the CENRAP States..................................... 1-3 

Table 1-2. MM5 Meteorological Model Configuration for 

 CENRAP 2002 Annual Modeling (Johnson, 2007)............................................. 1-9 

Table 1-3. SMOKE Emissions Model Configuration for CENRAP Annual Modeling ..... 1-10 

Table 1-4. CMAQ Air Quality Model Configuration for CENRAP Annual Modeling...... 1-11 

Table 1-5. CAMx Air Quality Model Configuration for CENRAP Annual Modeling ...... 1-12 

Table 1-6. Ground-level ambient data monitoring networks and 

 stations available in the CENRAP states for calendar year 

 2002 used in the model performance evaluation. .............................................. 1-20 

Table 2-1. Summary of SMOKE scripts ............................................................................... 2-4 

Table 2-2. Summary of SMOKE directories......................................................................... 2-5 

Table 2-3. Representative model days for 2002 base year simulation.................................. 2-6 

Table 2-4. 2002 modeled holidays ........................................................................................ 2-6 

Table 2-5. Assignments of months to four seasons for use of seasonal 

 inventory files in SMOKE ................................................................................... 2-7 

Table 2-6. Assignments of months to two seasons for use of seasonal 

 inventory files in SMOKE ................................................................................... 2-7 

Table 2-7. CENRAP Typ02G emissions categories ............................................................. 2-9 

Table 2-8. CENRAP anthropogenic emissions inventory contacts..................................... 2-11 

Table 2-9. New Canadian spatial surrogates....................................................................... 2-14 

Table 2-10. PM 2.5 speciation profiles for coal-burning sources ......................................... 2-16 

Table 2-11. Refueling SCCs removed from the non-WRAP U.S. 

 stationary-area inventory ................................................................................... 2-18 
September 2007 

vi 

Table 2-12. New Temporal Profile Assignments for CENRAP Area Source SCCs ............ 2-19 

Table 2-13. Non-road mobile-source inventory temporal configuration .............................. 2-24 

Table 2-14. Non-road agricultural emissions categories where the MRPO 

 Base K inventory was used instead of the CENRAP inventory in Iowa ........... 2-24 

Table 2-15. Non-road oil and gas development equipment categories 

 that Texas provided emissions to be used instead of the CENRAP inventory .. 2-25 

Table 2-16. Fugitive and road dust SCCs.............................................................................. 2-29 

Table 3-1. Model performance goals and criteria used to assist in 

 interpreting modeling results. .............................................................................. 3-3 

Table 4-1. Definition of IMPROVE PM Components from 

 Measured IMPROVE Species.............................................................................. 4-4 

Table 4-2. Mapping of CMAQ V4.5 modeled species concentrations to 

 IMPROVE PM components .............................................................................. 4-10 

Table 4-3. Observed and Modeled Extinction by Species Averaged 

 Across the Worst 20 Percent Days in 2002 at CACR........................................ 4-14 

Table 4-4. 2000-2004 Baseline, 2018 URP Point, and Projected 2018 

 Visibility and Percent of Meeting the 2018 URP Point for 

 the 2018 Base G and 2018 C1 Control Strategy CMAQ Simulations............... 4-22 

Table 5-1. Comparison of CENRAP and EPA MATS 2018 visibility 

 projections at CENRAP and nearby Class I areas ............................................. 5-12 

FIGURES 

Figure 1-1. Regional Planning Organizations engaged in Regional Haze Modeling............. 1-2 

Figure 1-2. National Inter-RPO 36 km modeling domain used 

 for the CENRAP 2002 annual SMOKE, CMAQ and CAMx modeling............ 1-13 

Figure 1-3. MM5 34 vertical layer definitions and scheme for 

 collapsing the 34 layers down to 19 layers for the 

 CENRAP CMAQ and CAMx 2002 annual modeling ....................................... 1-14 

Figure 1-4. 2018 visibility projections expressed as a percent of 

 meeting the 2018 URP point for the 2018 BaseG CMAQ 

 base case simulation using the EPA default (EPA, 2007) 

 Regular RRF and alternative projections procedures 

 that set the RRFs for CM=1.0 and CM&SOIL=1.0........................................... 1-23 

Figure 2-1. Flow diagram of major SMOKE processing steps needed 

 by all source categories........................................................................................ 2-2 

Figure 2-2. Flow diagram of SMOKE/BEIS2 processing steps............................................. 2-3 

Figure 2-3. Summary of Typ02G and Base18G SO2 emissions by 

 CENRAP state and major source sector (tons per year).................................... 2-39 

Figure 2-4. Summary of Typ02G and Base18G NOx emissions by 

 CENRAP state and major source sector (tons per year).................................... 2-40 

Figure 2-5. Summary of Typ02G and Base18G VOC emissions by 

 CENRAP state and major source sector (tons per year).................................... 2-40 

Figure 2-6. Summary of Typ02G and Base18G PM2.5 emissions by 

 CENRAP state and major source sector (tons per year).................................... 2-41 

Figure 2-7. Summary of Typ02G and Base18G PM10 emissions by 

 CENRAP state and major source sector (tons per year).................................... 2-41 
September 2007 

vii 

Figure 2-8. Summary of Typ02G and Base18G NH3 emissions by 

 CENRAP state and major source sector (tons per year).................................... 2-42 

Figure 2-9. Summary of Typ02G and Base18G CO emissions by 

 CENRAP state and major source sector (tons per year).................................... 2-42 

Figure 3-1. Monthly fractional bias (%) for sulfate (SO4) across the 

 CENRAP region for the CMAQ 2002 36 km Base F base case simulation........ 3-6 

Figure 3-2. Monthly fractional bias (%) for nitrate (NO3) across the 

 CENRAP region for the CMAQ 2002 36 km Base F base case simulation........ 3-7 

Figure 3-3. Monthly fractional bias (%) for organic matter carbon 

 (OMC) across the CENRAP region for the CMAQ 2002 

 36 km Base F base case simulation...................................................................... 3-8 

Figure 3-4. Monthly fractional bias (%) for elemental carbon (EC) 

 across the CENRAP region for the CMAQ 2002 36 km 

 Base F base case simulation............................................................................... 3-10 

Figure 3-5. Monthly fractional bias (%) for other PM2.5 (Soil) across 

 the CENRAP region for the CMAQ 2002 36 km Base F 

 base case simulation........................................................................................... 3-11 

Figure 3-6. Monthly fractional bias (%) for coarse mass (CM) across 

 the CENRAP region for the CMAQ 2002 36 km Base 

 F base case simulation. ...................................................................................... 3-12 

Figure 3-7. January 2002 performance at CENRAP CASTNet sites 

 for SO2 (top left), SO4 (top right), HNO3 (middle left), 

 NO3 (middle right), Total NO3 (bottom left) and NH4 (bottom right)............. 3-14 

Figure 3-8. January 2002 performance at CENRAP AQS sites for 

 SO2 (top left), NO2 (top right), O3 (bottom left) and CO (bottom right) ......... 3-15 

Figure 3-9. Daily extinction model performance at Caney Creek 

 (CACR), Arkansas for the worst (top) and best (bottom) 

 20 percent days during 2002.............................................................................. 3-18 

Figure 3-10. Daily extinction model performance at Upper Buffalo 

 (UPBU), Arkansas for the worst (top) and best (bottom) 

 20 percent days during 2002.............................................................................. 3-19 

Figure 3-11. Daily extinction model performance at Breton Island 

 (BRET), Louisiana for the worst (top) and best (bottom) 

 20 percent days during 2002.............................................................................. 3-20 

Figure 3-12. Daily extinction model performance at Boundary Waters 

 (BOWA), Minnesota for the worst (top) and best (bottom) 

 20 percent days during 2002.............................................................................. 3-21 

Figure 3-13. Daily extinction model performance at Voyageurs 

 (VOYA), Minnesota for the worst (top) and best (bottom) 

 20 percent days during 2002.............................................................................. 3-22 

Figure 3-14. Daily extinction model performance at Hercules Glade 

 (HEGL), Missouri for the worst (top) and best (bottom) 

 20 percent days during 2002.............................................................................. 3-23 

Figure 3-15. Daily extinction model performance at Mingo (MING), 

 Missouri for the worst (top) and best (bottom) 

 20 percent days during 2002.............................................................................. 3-24 
September 2007 

viii 

Figure 3-16. Daily extinction model performance at Wichita Mountains 

 (WIMO), Oklahoma for the worst (top) and best (bottom) 

 20 percent days during 2002.............................................................................. 3-25 

Figure 3-17. Daily extinction model performance at Big Bend (BIBE), 

 Texas for the worst (top) and best (bottom) 20 percent days during 2002........ 3-26 

Figure 3-18. Daily extinction model performance at Guadalupe Mountains 

 (GUMO), Texas for the worst (top) and best (bottom) 

 20 percent days during 2002.............................................................................. 3-27 

Figure 4-1. Comparisons of observed light extinction with reconstructed 

 light extinction using the new (left) and original (right) 

 IMPROVE equations at the Great Smoky Mountains National Park.................. 4-7 

Figure 4-2. Linear Glidepath for Caney Creek (CACR), Arkansas 

 that linearly connects the 26.36 dv Baseline Conditions in 2004 

 with the 11.58 dv Natural Conditions in 2064 resulting in 

 a 22.91 dv 2018 URP Point.................................................................................. 4-9 

Figure 4-3a. 2018 Visibility Projections and 2018 URP Glidepaths in 

 Deciview for Caney Creek (CACR), Arkansas and Worst 

 20 Percent (W20%) days Using 2002/2018 Base 

 G CMAQ 36 km Modeling Results. .................................................................. 4-14 

Figure 4-3b. 2018 Visibility Projections and 2018 URP Glidepaths 

 in Deciview for CACR, Arkansas and Best 20 Percent (B20%) 

 days Using 2002/2018 Base G CMAQ 36 m Modeling Results. ...................... 4-15 

Figure 4-3c. Comparison of Observed (left) and 2002 Base G Modeled 

 (right) Daily Extinction for Caney Creek (CACR), 

 Arkansas and Worst 20 Percent (W20%) days in 2002..................................... 4-15 

Figure 4-3d. Differences in Modeled 2002 and 2018 Base G CMAQ 

 Results (2018-2002) Daily Extinction for Caney Creek (CACR), 

 Arkansas and Worst 20 Percent (W20%) Days in 2002.................................... 4-16 

Figure 4-4. 2018 Base G CMAQ Visibility Projections for CENRAP 

 and Nearby Class I areas Using DotPlots that Express 2018 

 Visibility as a Percentage of Meeting the 2018 URP Point 

 On the Deciview Linear Glidepath .................................................................... 4-17 

Figure 4-5. Absolute Model Estimated Changes in Extinction (Mm-1) 

 by PM Species for Class I Areas in the CENRAP region (top) 

 and Near the CENRAP region (bottom). ........................................................... 4-18 

Figure 4-6. Percent Change In Modeled Extinction by PM Species 

 Averaged Across the 2002 Worst 20 Percent Days for 

 Class I areas in the CENRAP region (top) and 

 Near the CENRAP region (bottom)................................................................... 4-19 

Figure 4-7. CMAQ-Estimated Reductions in Annual Average 

 SO4 (left) and NO3 (right) Fine Particle Concentrations Between 

 the 2018 Base G Base Case and 2018 Base G C1 Control Strategy Case......... 4-20 

Figure 4-8. 2018 Visibility Projections as a Percentage of Meeting 

 the 2018 URP Point (i.e., DotPlot) for the 2018 Base G 

 and 2018 Base G C1 Control Strategy Emission Scenarios. ............................. 4-21 

Figure 5-1. DotPlot comparing the CENRAP, VISTAS, MRPO and 

 WRAP 2018 visibility projections expressed as a percentage 

 of achieving the 2018 URP goal. ......................................................................... 5-3 
September 2007 

ix 

Figure 5-2. 2002 Base F SO2 emissions (left) as LOG10(tons/year) 

 and differences in 2018 and 2002 Base F SO2 emissions (tons/year)................. 5-3

Figure 5-3. Exampled back trajectories to Voyageurs National 

 Park for two of the worst 20 percent days from 2002: December 

 13, 2002 (Julian Day 347) and November 28, 2002 (Julian Day 332) ................ 5-4

Figure 5-4. 2018 Visibility Projections and 2018 URP Glidepaths 

 in extinction (Mm-1) for Caney Creek (CACR), 

 Arkansas and Worst 20% (W20%) days using 2002/2018 

 Base G CMAQ 36 km modeling results .............................................................. 5-6 

Figure 5-5. CMAQ 2018 Base G visibility projections and \ 

 comparison of ability to achieve the 2018 URP point 

 using the EPA default deciview and alternative total extinction Glidepaths....... 5-6 

Figure 5-6. 2018 Visibility Projections and 2018 URP Glidepaths 

 for SO4 (top left), NO3 (top right), EC (middle left), 

 OMC (middle right), Soil (bottom left) and CM (bottom right) 

 in extinction (Mm-1) for Caney Creek (CACR), 

 Arkansas and Worst 20 Percent Days using 2002/2018 

 Base G CMAQ 36 km modeling results .............................................................. 5-9 

Figure 5-7. Ability of total and species specific 2018 visibility 

 projections to achieve 2018 URP points............................................................ 5-10 

Figure 5-8. 30 source regions used in the CENRAP 2002 and 

 2018 CAMx PSAT PM source apportionment modeling.................................. 5-14 

Figure 5-9. PSAT source category by PM species contributions 

 to the average 2000-2004 Baseline and 2018 projected 

 extinction (Mm-1) for the worst 20 percent visibility 

 days at Caney Creek (CACR), Arkansas ........................................................... 5-16 

Figure 5-10. PSAT source region by source category contributions 

 to the average 2000-2004 Baseline and 2018 projected 

 extinction (Mm-1) for the worst 20 percent visibility days 

 at Caney Creek (CACR), Arkansas. .................................................................. 5-17 

Figure 5-11. PSAT source category by PM species contributions 

 to the average 2000-2004 Baseline and 2018 projected 

 extinction (Mm-1) for the best 20 percent visibility days 

 at Caney Creek (CACR), Arkansas. .................................................................. 5-17 

Figure 5-12. Sensitivity of 2018 visibility projections to various 

 methods that assume all CM, all CM and Soil and all CM 

 and part of the Soil is natural ............................................................................. 5-21 

Figure 5-13. Comparison of CAMx 2018 visibility projections with 

 2018 URP points for CENRAP and nearby Class I areas.................................. 5-22 

Figure 5-14. Comparison of EPRI Harvard GEOS-Chem global 

 chemistry (top) and CENRAP PSAT (bottom) international 

 source contributions to ammonium sulfate at Class I areas............................... 5-25 

Figure 5-15. Comparison of EPRI Harvard GEOS-Chem global 

 chemistry (top) and CENRAP PSAT (bottom) international 

 source contributions to ammonium nitrate, organic 

 carbon mass (OCM or OMC) and elemental carbon (EC) at Class I areas. ...... 5-26 
September 2007 

x 

Figure 5-16. Elimination of international sources from 2018 

 visibility projections and comparison with 2018 URP 

 point at CENRAP Class I areas ......................................................................... 5-29 

Figure 5-17. Glidepaths and 2018 visibility projections based on visibility 

 due to U.S. anthropogenic emissions at CENRAP Class I areas....................... 5-32 

Figure 5-18. Comparison of 2018 Base G visibility projections 

 using the New (NIA) and Old (OIA) IMPROVE algorithms 

 expressed as a percentage of achieving the 2018 URP 

 point visibility improvements ............................................................................ 5-33 

Figure 5-19a. Time series of observed IMPROVE reconstructed 

 light extinction (New IMPROVE) at Caney Creek (CACR), 

 Arkansas for the average of the Worst 20 Percent days (top), 

 Best 20 Percent days (middle) days and all IMPROVE 

 sampling days during the period of record ........................................................ 5-35 

Figure 5-19b. Time series of observed IMPROVE reconstructed 

 light extinction (New IMPROVE) at Upper Buffalo (UPBU), 

 Arkansas for the average of the Worst 20 Percent days (top), 

 Best 20 Percent days (middle) days and all IMPROVE 

 sampling days during the period of record. ....................................................... 5-36 

Figure 5-19c. Time series of observed IMPROVE reconstructed light 

 extinction (New IMPROVE) at Breton Island (BRET), 

 Louisiana for the average of the Worst 20 

 Percent days (top), Best 20 Percent days (middle) 

 days and all IMPROVE sampling days during the period of record ................. 5-37 

Figure 5-19d. Time series of observed IMPROVE reconstructed 

 light extinction (New IMPROVE) at Boundary Waters 

 (BOWA), Minnesota for the average of the Worst 20 

 Percent days (top), Best 20 Percent days (middle) days and 

 all IMPROVE sampling days during the period of record ................................ 5-38 

Figure 5-19e. Time series of observed IMPROVE reconstructed 

 light extinction (New IMPROVE) at Voyageurs (VOYA), 

 Minnesota for the average of the Worst 20 Percent days (top), 

 Best 20 Percent days (middle) days and all IMPROVE 

 sampling days during the period of record. ....................................................... 5-39 

Figure 5-19f. Time series of observed IMPROVE reconstructed 

 light extinction (New IMPROVE) at Hercules Glade 

 (HEGL), Missouri for the average of the Worst 20 Percent 

 days (top), Best 20 Percent days (middle) days and all 

 IMPROVE sampling days during the period of record. .................................... 5-40 

Figure 5-19g. Time series of observed IMPROVE reconstructed 

 light extinction (New IMPROVE) at Mingo (MING), 

 Missouri for the average of the Worst 20 Percent days (top), 

 Best 20 Percent days (middle) days and all IMPROVE 

 sampling days during the period of record. ....................................................... 5-41 
September 2007 

xi 

Figure 5-19h. Time series of observed IMPROVE reconstructed 

 light extinction (New IMPROVE) at Wichita Mountains 

 (WIMO), Oklahoma for the average of the Worst 20 Percent 

 days (top), Best 20 Percent days (middle) days and all 

 IMPROVE sampling days during the period of record. .................................... 5-42 

Figure 5-19i. Time series of observed IMPROVE reconstructed 

 light extinction (New IMPROVE) at Big Bend (BIBE), 

 Texas for the average of the Worst 20 Percent days (top), 

 Best 20 Percent days (middle) days and all IMPROVE 

 sampling days during the period of record. ....................................................... 5-43 

Figure 5-19j. Time series of observed IMPROVE reconstructed 

 light extinction (New IMPROVE) at Guadalupe Mountains 

 (GUMO), Texas for the average of the Worst 20 Percent days 

 (top), Best 20 Percent days (middle) days and all 

 IMPROVE sampling days during the period of record ..................................... 5-44 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-1 

1.0 INTRODUCTION 

This Technical Support Document (TSD) describes the Central Regional Air Planning 

Association (CENRAP) regional emissions and air quality modeling to support the central states 

Regional Haze Rule (RHR) State Implementation Plans (SIPs). The CENRAP 2002 annual 

emissions and air quality modeling was performed by the contractor team of ENVIRON 

International Corporation (ENVIRON) and the University of California at Riverside (UCR). 

1.1 Background 

The 1977 Clean Air Act Amendments (CAAA) added a new Section 169A for the protection of 

visibility in Federal Class I areas (specific national parks, wilderness areas and wildlife refuges). 

Section 169A(a)(1) of the CAAA established the national goal for visibility protection: 

“Congress hereby declares as a national goal the prevention of any future, and the remedying of 

any existing, impairment of visibility in mandatory class I Federal areas which impairment 

results from manmade air pollution.” The CAAA require States to submit SIPs containing 

emission limits, schedules of compliance and to “promulgate regulations to assure reasonable 

progress toward meeting the national goal” (Section 169A(a)(4)). In response to these mandates 

EPA promulgated the Regional Haze Rule (RHR) on July 1, 1999 that requires States to 

“establish goals (expressed in deciviews) that provide for reasonable progress towards achieving 

natural visibility conditions” at Class I areas. The States’ RHR SIPs are due December 17, 2007 

and an important component of the SIP will be the 2018 Reasonable Progress Goals (RPGs) 

toward achieving natural conditions in 2064. Regional air quality models are used to project 

visibility to 2018 to determine the level of visibility improvement that is expected to be achieved 

in 2018. This information, along with other sources, can be used by the states to assist in setting 

their 2018 RPGs. 

CENRAP is one of five Regional Planning Organizations (RPOs) that have responsibility for 

coordinating development of SIPs and Tribal Implementation Plans (TIPs) in selected areas of 

the U.S. to address the requirements of the RHR. CENRAP is a regional partnership of states, 

tribes, federal agencies, stakeholders and citizen groups established to initiate and coordinate 

activities associated with the management of regional haze and other air quality issues within the 

CENRAP states. The CENRAP region includes states and tribal lands located within the 

boundaries of Arkansas, Iowa, Kansas, Louisiana, Minnesota, Missouri, Nebraska, Oklahoma 

and Texas. 

The CENRAP Emissions and Air Quality Modeling Team is composed of staff from ENVIRON 

and UCR, with assistance and coordination from the CENRAP states, tribes, federal agencies and 

stakeholders. The ENVIRON/UCR Team performs the emissions and air quality modeling 

simulations for states and tribes within the CENRAP region, providing analytical results used in 

developing implementation plans under the RHR. Figure 1-1 shows the states included in each of 

the five RPOs in the U.S., including CENRAP. Table 1-1 lists the Class I areas within the 

CENRAP states. 

CENRAP is performing emissions and air quality modeling to project visibility to 2018. The 

modeling results will be used to determine the level of visibility improvement expected in 2018 
 

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under various emission scenarios. States will use these results to assist in determining their 

2018 RPGs toward achieving natural conditions in 2064. 

Figure 1-1. Regional Planning Organizations engaged in Regional Haze Modeling.
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-3 

Table 1-1. Federal Mandated Class I Areas in the CENRAP States. 

Class I Area Acreage 

Federal Land 

Manager 

Public 

Law 

Arkansas

Caney Creek Wilderness Area 14,460 USDA-FS 93-622 

Upper Buffalo Wilderness Area 12,018 USDA-FS 93-622 

Louisiana

Breton Wilderness Area 5,000+ USDI-FWS 93-632 

Minnesota

Boundary Waters Canoe Area Wilderness 810,088 USDA-FS 99-577 

Voyageurs National Park 114,964 USDI-NP 99-261 

Missouri

Hercules-Glade Wilderness Area 12,314 USDA-FS 94-557 

Mingo Wilderness Area 8,000 USDI-FWS 95-557 

Oklahoma

Wichita Mountains Wilderness 8,900 USDI-FWS 91-504 

Texas

Big Bend National Park 708,118 USDI-NP 74-157 

Guadalupe Mountains National Park 76,292 USDI-NP 89-667 

1.2 CENRAP Organizational Structure and Work Groups 

The governing body of CENRAP is the Policy Oversight Group (POG) that is made up of voting 

members representing states and tribes within the CENRAP region and non-voting members 

representing local agencies, the EPA and other federal agencies. The work of CENRAP is 

accomplished through five standing workgroups: 

• Monitoring; 

• Emissions Inventory; 

• Modeling; 

• Communications; and 

• Implementation and Control Strategies. 

Participation in workgroups is open to all interested parties and the POG may form additional ad 

hoc workgroups to address specific issues (e.g., a Data Analysis workgroup was formed). 

The RHR requires the states, and the tribes that may elect to, submit the first SIPs and TIPs that 

address progress toward natural conditions at federally mandated Class I areas by December 17, 

2007. 40 CFR 51.308 (Section 308) discusses the following four core requirements to be 

included in SIPs/TIPs and Best Available Retrofit Technology (BART) requirements: 

1. Reasonable progress goals; 

2. Calculations of baseline and natural visibility conditions; 

3. A Long-term strategy for regional haze; 

4. A Monitoring strategy and other implementation plan requirements; and 

5. BART requirements for regional haze visibility impairment. 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-4 

One of CENRAP’s goals is to provide support to states and tribes to meet each of these 

requirements of the RHR and to develop scientifically supportable, economical and effective 

control strategies that the states and tribes may adopt to reduce anthropogenic effects on 

visibility impairment at Class I areas. One component of CENRAP’s support to states and tribes 

as part of compliance with the RHR is performing emissions and air quality modeling. These 

activities were implemented to: 

• obtain a better understanding of the causes of visibility impairment and to identify 

potential mitigation measures for visibility impairment at Class I areas; 

• to evaluate the effects of alternative control strategies for improving visibility; 

and 

• to project future-year air quality and visibility conditions. 

In October 2004, CENRAP selected the team of ENVIRON and UCR to perform their Emissions 

and Air Quality Modeling. 

The CENRAP Emissions and Air Quality Modeling Team performs regional haze analyses by 

operating regional scale, three-dimensional air quality models that simulate the emissions, 

chemical transformations, and transport of gaseous and particulate matter (PM) species and 

consequently the effects on visibility in Class I Areas in the central U.S. A key element of this 

work includes the integration of emissions inventories and emissions models with regional 

transport models. The general services provided by the CENRAP Emissions and Air Quality 

Modeling Team include, but are not limited to: 

• Emissions processing and modeling; 

• Air quality and visibility modeling simulations; 

• Analysis, display, and reporting of modeling results; and 

• Storage/quality assurance of the modeling input and output files. 

The CENRAP 2002 annual Emissions and Air Quality Modeling Team performs work for the 

CENRAP Modeling Workgroup through direction from the CENRAP Technical Director and 

CENRAP Executive Director. 

1.3 Overview of 2002 Annual Emissions and Air Quality Modeling Approach 

The CENRAP 2002 annual emissions and air quality modeling was initiated on October 16, 2004 

and involved the preparation of numerous databases, model simulations, presentations and 

reports. Much of the modeling analyses have been posted to the CENRAP modeling website at: 

http://pah.cert.ucr.edu/aqm/cenrap/index.shtml. There were numerous versions and iterations of 

the modeling and interim results. The results presented in this TSD focus on the final modeling 

results and key findings in their development. The reader is referred to the modeling website for 

interim products. 

1.3.1 Modeling Protocol 

A Modeling Protocol was prepared at the outset of the study to serve as a road map for 

performing the CENRAP emissions and air quality modeling and to communicate the modeling 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-5 

plans to the CENRAP participants. The Modeling Protocol was prepared following EPA 

guidance for preparation at the time it was prepared (EPA, 1991; 1999, 2001) and took into 

account CENRAP’s long-term plan (CENRAP, 2003) and the modeling needs of the RHR SIPs. 

The first version (Version 1.0) of the Modeling Protocol was dated November 19, 2004. Based 

on comments received from CENRAP, the Modeling Protocol was updated to the current 

Version 2.0 (Morris et al., 2004a) that was dated December 8, 2004. This Modeling Protocol can 

be found on the CENRAP modeling Website at: 

http://pah.cert.ucr.edu/aqm/cenrap/docs/CENRAP_Draft2.0_Modeling_Protocol_120804.pdf

1.3.2 Quality Assurance Project Plan (QAPP) 

A Quality Assurance Project Plan (QAPP) was prepared for the CENRAP emissions and air 

quality modeling study that described the quality management functions performed by the 

modeling team. The QAPP was prepared and was based on the national consensus standards for 

quality assurance (ANSI/ASQC, 1994), followed EPA’s guidelines for quality assurance project 

plans for modeling (EPA, 2002) and for QAPPs (EPA, 2001) and took into account the 

recommendations from the North American Research Strategy for Tropospheric Ozone 

(NARSTO) Quality Handbook for modeling projects (NARSTO, 1998). The EPA and NARSTO 

guidance documents were developed specifically for modeling projects, which have different 

quality assurance concerns than environmental monitoring data collection projects. The work 

performed in this project involves modeling at the basic research level and for 

regulatory/planning applications. In order to use model outputs for these purposes, it must be 

established that each model is scientifically sound, robust, and defensible. This is accomplished 

by following a project planning process that incorporates the following elements as described in 

the EPA modeling guidance document: 

• A systematic planning process including identification of assessments and related 

performance criteria; 

• Peer reviewed theory and equations; 

• A carefully designed life-cycle development process that minimizes errors; 

• Documentation of any changes from original plans; 

• Clear documentation of assumptions, theory, and parameterization that is detailed enough 

so others can understand the model output; 

• Input data and parameters that are accurate and appropriate for the analysis; and 

• Output data that can be used to help inform decision makers. 

The CENRAP QAPP can be found at: 

http://pah.cert.ucr.edu/aqm/cenrap/docs/CENRAP_QAPP_Nov_24_2004.pdf). 

A key component of the CENRAP emissions and air quality modeling QAPP was the graphical 

display of model inputs and outputs and multiple peer-review of each step of the modeling 

process. This was accomplished through use of the CENRAP modeling website where modelers 

posted displays of work products (e.g., emissions plots, model outputs, etc.) for review by the 

CENRAP modeling team, modeling workgroup and others. This website can be found at: 

http://pah.cert.ucr.edu/aqm/cenrap/index.shtml. 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-6 

1.3.3 Model Selection 

The selection of the meteorological, emissions and air quality models for the CENRAP regional 

haze modeling was based on a review of previous regional haze modeling studies performed in 

the CENRAP region (e.g., Pitchford et al., 2004; Pun, Chen and Seigneur, 2004; Tonnesen and 

Morris 2004) as well as elsewhere in the United States (e.g., Morris et al, 2004a; Tonnesen et al., 

2003; Baker, 2004). The CENRAP emissions and air quality Modeling Protocol (Morris et al., 

2004a) provides details on the justification for model selection and the formulation of the 

different models. Based on previous work (e.g., CENRAP, WRAP, VISTAS, MRPO, BRAVO 

and EPA), CENRAP selected the following models for use in modeling PM and regional haze in 

the central states: 

3⁄4 MM5: The Pennsylvania State University/National Center for Atmospheric Research 

(PSU/NCAR) Mesoscale Meteorological Model (MM5 Version 3.6 MPP) is a nonhydrostatic, prognostic meteorological model routinely used for urban- and regional-scale 

photochemical, fine particulate, and regional haze regulatory modeling studies (Anthes and 

Warner, 1978; Chen and Dudhia, 2001; Stauffer and Seaman, 1990, 1991; Xiu and Pleim, 

2000). 

3⁄4 SMOKE: The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is an 

emissions modeling system that generates hourly gridded speciated emission inputs of 

mobile, non-road, area, point, fire and biogenic emission sources for photochemical grid 

models. (Coats, 1995; Houyoux and Vukovich, 1999). As with most ‘emissions models’, 

SMOKE is principally an emission processing system and not a true emissions modeling 

system in which emissions estimates are simulated from ‘first principles’. This means that, 

with the exception of mobile and biogenic sources, its purpose is to provide an efficient tool 

for converting an existing base emissions inventory data into the hourly, gridded, speciated, 

and formatted emission files required by an air quality model. 

3⁄4 CMAQ: EPA’s Models-3/Community Multiscale Air Quality (CMAQ) modeling system is 

a ‘One-Atmosphere’ photochemical grid model capable of addressing ozone, PM, visibility 

and acid deposition at a regional scale for extended periods of time (Dennis, et al., 1996; 

Byun et al., 1998a; Byun and Ching, 1999, Pleim et al., 2003). 

3⁄4 CAMx: ENVIRON’s Comprehensive Air Quality Model with Extensions (CAMx) 

modeling system is also a state-of-science ‘One-Atmosphere’ photochemical grid model 

capable of addressing ozone, PM, visibility and acid deposition at a regional scale for 

extended periods of time. (ENVIRON, 2006). 

1.3.3.1 MM5 Meteorological Model Configuration for CENRAP Annual Modeling 

Application of the MM5 for the 2002 annual modeling on a 36 km grid for the continental US 

was performed by the Iowa Department of Natural Resources (IDNR; Johnson, 2007). Details of 

the 2002 36 km MM5 model application and evaluation procedures carried out by IDNR may be 

found in Johnson, 2007. Application of the MM5 model on a 12 km grid covering the Central 

States for portions of 2002 was performed by EPA Region VII and the Texas Commission on 

Environmental Quality (TCEQ). 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-7 

The MM5 (Version 3.63) configuration used in the generation of the meteorological modeling 

datasets consists of the following (see Table 1-2 for more details): 

3⁄4 36 km grid with 34 vertical layers; 

3⁄4 12 km nested grid for episodic modeling; 

3⁄4 For 12 km runs use two way nesting (without feedback) within the 36 km grid; 

3⁄4 Initialization and boundary conditions from Eta analysis fields; 

o Eta 3D and surface analysis data (ds609.2); 

o Not using NCEP global tropospheric SST data (ds083.0) ; 

o Observational enhancement (LITTLE_R) 

 NCEP ADP surface obs (ds464.0) 

 NCEP ADP upper-air obs (ds353.4) 

3⁄4 Pleim-Xiu (P-X) land-surface model (LSM); 

3⁄4 Pleim-Chang Asymmetric Convective Mixing (ACM) PBL model; 

3⁄4 Kain-Fritsch 2 cumulus parameterization; 

3⁄4 Mixed phase (Reisner 1) cloud microphysics; 

3⁄4 Rapid Radiative Transfer Model (RRTM) radiation; 

3⁄4 No Shallow Convection (ISHALLO=0); 

3⁄4 Standard 3D FDDA analysis nudging outside of PBL; and 

3⁄4 Surface nudging of the winds only. 

1.3.3.2 SMOKE Emissions Model Configuration for CENRAP Annual Modeling 

SMOKE supports area, mobile, fire and point source emission processing and includes biogenic 

emissions modeling through a rewrite of the Biogenic Emission Inventory System, version 3 

(BEIS3) (see, http://www.epa.gov/ttn/chief/software.html#pcbeis). SMOKE has been available 

since 1996, and has been used for emissions processing in a number of regional air quality 

modeling applications. In 1998 and 1999, SMOKE was redesigned and improved with the 

support of the U.S. Environmental Protection Agency (EPA), for use with EPA's Models3/CMAQ (http://www.epa.gov/asmdnerl/models3). The primary purposes of the SMOKE 

redesign were support of: (a) emissions processing with user-selected chemical mechanisms and 

(b) emissions processing for reactivity assessments. 

As an emissions processing system, SMOKE has far fewer ‘science configuration’ options 

compared with the MM5 and CMAQ models. Table 1-3 summarizes the version of the SMOKE 

system that was used and the sources of data that were employed in constructing the required 

modeling inventories. 

1.3.3.3 CMAQ Air Quality Model Configuration for CENRAP Annual Modeling 

CENRAP used CMAQ Version 4.5 with the “SOAmods enhancement”, described below, and 

used the model configuration as shown in Table 1-4. The model was set up and exercised on the 

same 36 km grid that was used by WRAP and VISTAS, the 36 km RPO national grid. CENRAP 

performed 12 km CMAQ sensitivity tests and found little change in model performance with a 

large penalty in computation time. Consequently, at the February 7, 2006 CENRAP Modeling 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-8 

Workgroup Meeting a decision was made to proceed with the CENRAP emissions and air 

quality modeling using just the 36 km national RPO grid (Morris et al., 2006a). 

Initial CMAQ 2002 simulations performed by VISTAS found that the model greatly 

underestimates organic mass carbon (OMC) concentrations, especially in the summer. A review 

of the CMAQ formulation found that it failed to treat Secondary Organic Aerosol (SOA) 

formation from sesquiterpenes and isoprene and also failed to account for the fact that SOA can 

become polymerized so that it is no longer volatile and stays in the particle form. Thus, VISTAS 

updated the CMAQ SOA module to include these missing processes and found much improved 

OMC model performance (Morris et al., 2006c). CENRAP tested the CMAQ Version 4.5 with 

SOAmods enhancement and found it performed much better for OMC than the standard versions 

of CMAQ Version 4.5. Therefore, CMAQ Version 4.5, with the enhanced SOAmods (Morris et 

al., 2006c), was adopted for the CENRAP modeling. CMAQ Version 4.5 is available from the 

CMAS center (www.cmascenter.org). 

1.3.3.4 CAMx Air Quality Model Configuration for CENRAP Annual Modeling 

CAMx Version 4.40 was applied using similar options as used by CMAQ. CAMx was used 

initially in side-by-side comparisons with CMAQ. Comparative model performance results and 

other factors for CAMx V4 and CMAQ V4.4 with SOAmods were presented at the February 7, 

2006 CENRAP modeling workgroup meetings that found (Morris et al., 2006b): 

• No one model was consistently performing better than the other over all species and 

averaging times. 

• Both models performed well for sulfate. 

• CMAQ’s winter nitrate over-prediction tendency not as large as CAMx’s. 

• CAMx performed slightly better than CMAQ for elemental carbon (EC). 

• CMAQ performed much better than CAMx for organic mass carbon (OMC). 

• Both models over-predicted Soil and under-predicted coarse mass (CM). 

• CMAQ ran faster than CAMx due to MPI multi-processing capability. 

• CAMx required much less disk space than CMAQ. 

Based on these factors, CMAQ was selected as the lead air quality model for the CENRAP 

regional haze modeling with CAMx the secondary corroborative model. However, CAMx also 

contained a PM Source Apportionment Technology (PSAT) capability that was used widely in 

the CENRAP modeling. Table 1-4 lists the main CAMx configuration used for the CENRAP 

annual modeling that was selected, in part, to be consistent with the CMAQ model configuration 

(Table 1-4). One exception to this was that the CAMx PSAT simulations used the Bott 

advection solver rather than the PPM advection solver. The PPM advection solver is typically 

used in the standard CAMx and CMAQ runs. Bott, however, is more computationally efficient 

and the high computational requirements of the CAMx PSAT runs dictated this choice. 
 

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Table 1-2. MM5 Meteorological Model Configuration for CENRAP 2002 Annual Modeling 

(Johnson, 2007).

Science Options Configuration Details/Comments 

Model Code MM5 version 3.63 Grell et al., 1994 

Horizontal Grid Mesh 36 km 

 36 km grid 165 x 129 dot points RPO MM5 Grid 

Vertical Grid Mesh 34 layers 

Vertically varying; sigma pressure 

coordinate system 

Grid Interaction No Feedback IFEED=0 

Initialization Eta first guess fields/LittleR 

Boundary Conditions Eta first guess fields/LittleR 

Microphysics Reisner I Mixed Ice Look up table 

Cumulus Scheme Kain-Fritsch 2 On 36 and 12 km Grids 

Planetary Boundary Layer ACM PBL 

Radiation RRTM 

Vegetation Data USGS 24 Category Scheme 

Land Surface Model Pleim-Xiu Land Surface Model (LSM) 

Shallow Convection None 

Sea Surface Temperature Eta Skin Spatially varying 

Thermal Roughness Garratt 

Snow Cover Effects None 

4D Data Assimilation Analysis Nudging on 36 and 12 

Surface Nudging Wind Field Only 

Integration Time Step 90 seconds 

Simulation Periods Annual 2002 for 36 km 12 km episodic only 

Platform Linux Cluster Done at IDNR1

 

1 Twelve km episodic modeling completed by EPA Region VII and the Texas Commission on Environmental 

Quality.
 

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Table 1-3. SMOKE Emissions Model Configuration for CENRAP Annual Modeling.

Emissions Component Configuration Details/Comments 

Emissions Model SMOKE Version 2.3 

Several versions of SMOKE used during course 

of the study 

Horizontal Grid Mesh 36 km 

36 km grid 148 x 112 cells RPO National Grid 

Area Source Emissions 

CENRAP Domain: CENRAP State 

2002 EI 

Updated '02 developed by CENRAP states 

(Pechan, 2005d,e) 

 

Other States: '02 NEI augmented 

with other 2002 

Generated from EPA NEI02 v.1 and RPO 

interaction (Pechan, 2005c) 

On-Road Mobile Sources 

CENRAP Domain: CENRAP VMT 

data 

Updated '02 developed by CENRAP states 

(Reid et al., 2004a) 

 

Other States: EPA '02 NEI 

augmented with other 2002 

Generated from EPA NEI02 v.1 and RPO 

interaction (Pechan, 2005c) 

Point Sources 

CENRAP Domain: CENRAP State 

2002 EI 

Updated '02 developed by CENRAP states and 

stakeholders (Pechan, 2005a,b) 

 

Other States: EPA '02 NEI 

augmented with other 2002 

Generated from EPA NEI02 v.1 and RPO 

interaction (Pechan, 2005c) 

Off-Road Mobile Sources 

CENRAP Domain: CENRAP State 

2002 EI 

Updated '02 developed by CENRAP states 

(Pechan, 2005d,e) 

 

Other States: EPA '02 NEI 

augmented with other 2002 

Generated from EPA NEI02 v.1 and RPO 

interaction (Pechan, 2005c) 

Biogenic Sources SMOKE BEIS-3 BELD3 vegetative database 

Mexican Sources 1999 Emissions for 2002 and 2018

http://www.epa.gov/ttn/chief/net/mexico.html; 

(ERG, 2006) 

Canadian Sources 

2000 Emissions for 2002 and 2020 

Emissions for 2018 http://www.epa.gov/ttn/chief/net/canada.html 

Temporal Adjustments Seasonal, day, hour 

Based on latest collected information and CEMbased profiles 

Chemical Speciation 

Revised CBM-IV Chemical 

Speciation Updated January 2004 

Gridding 

Revised EPA Spatial Surrogates 

Used 

Gridding of surrogates from 

http://www.epa.gov/ttn/chief/emch/spatial/ 

Growth and Controls CENRAP developed Pechan (2005a,b) 

Quality Assurance QA Tools in SMOKE 2.0 

Follow QAPP (Morris and Tonnesen, 2004) and 

QA refinements (Morris and Tonnesen, 2006) 

Simulation Periods Annual 2002 for 36 km Episodic periods at 12 km 
 

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Table 1-4. CMAQ Air Quality Model Configuration for CENRAP Annual Modeling.

Science Options Configuration Details/Comments 

Model Code 

CMAQ Version 4.5 w/ 

SOAmods 

Secondary Organic Aerosol 

enhancements as described by Morris 

et al., (2006c) 

Horizontal Grid Mesh 36 km annual 

36 km covering continental U.S; some 

episodic 12 km sensitivity runs were 

also performed 

36 km grid 148 x 112 cells RPO National Grid 

Vertical Grid Mesh 19 Layers First 17 layers sync'd w/ MM5 

Grid Interaction One-way nesting 

Initial Conditions ~15 days full spin-up Separately run 4 quarters of 2002 

Boundary Conditions 

2002 GEOS-CHEM dayspecific 2002 GEOS-CHEM day specific 3-hour 

average data 

Emissions 

Baseline Emissions Processing 

See SMOKE model 

configuration 

MM5 Meteorology input to SMOKE, 

CMAQ 

Sub-grid-scale Plumes No Plume-in-Grid (PinG) 

Chemistry 

Gas Phase Chemistry CBM-IV 

Aerosol Chemistry AE3/ISORROPIA 

Secondary Organic Aerosols 

Secondary Organic Aerosol 

Model (SORGAM) w/ 

SOAmods update 

Schell et al., (2001); Morris et al., 

(2006c) 

Cloud Chemistry 

RADM-type aqueous 

chemistry Includes subgrid cloud processes 

N2O5 Reaction Probability 0.01 – 0.001 

Meteorological Processor MCIP Version 2.3 

Includes dry deposition and snow cover 

updates 

Horizontal Transport 

Numerical Scheme PPM advection solver 

Eddy Diffusivity Scheme 

K-theory with Kh grid size 

dependence 

Multiscale Smagorinsky (1963) 

approach 

Vertical Transport 

Eddy Diffusivity Scheme K-theory 

Diffusivity Lower Limit Kzmin = 0.1 to 1.0 Land use dependent Kzmin 

Deposition Scheme M3dry 

Directly linked to Pleim-Xiu Land 

Surface Model parameters 

Numerics 

Gas Phase Chemistry Solver 

Euler Backward Iterative 

(EBI) solver 

Horizontal Advection Scheme 

Piecewise Parabolic Method 

(PPM) scheme 

Simulation Periods Annual 2002 for 36 km Episodic periods at 12 km 

Integration Time Step Calculated Internally 15 minute coupling time step 
 

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Table 1-5. CAMx Air Quality Model Configuration for CENRAP Annual Modeling.

Science Options Configuration Details 

Model Code CAMx Version 4.40 Available at: www.camx.com

Horizontal Grid Mesh 36 km annual 36 km covering continental U.S 

36 km grid 148 x 112 cells 

Vertical Grid Mesh 19 Layers 17 Layers sync'd w/ MM5 

Grid Interaction Two-way nesting 

Initial Conditions ~15 days full spin-up Separately run 4 quarters of 2002 

Boundary Conditions 

2002 GEOS-CHEM dayspecific 2002 GEOS-CHEM day specific 3-hour 

average data 

Emissions 

Baseline Emissions Processing 

See SMOKE model 

configuration 

MM5 Meteorology input to SMOKE, 

CAMx 

Sub-grid-scale Plumes No Plume-in-Grid (PinG) Consistent with CMAQ 

Chemistry 

Gas Phase Chemistry CBM-IV with Isoprene updates 

Aerosol Chemistry ISORROPIA equilibrium 

Dynamic and hybrid also available but 

not used 

Secondary Organic Aerosols SOAP 

Cloud Chemistry 

RADM-type aqueous 

chemistry 

Alternative is CMU multi-section 

aqueous chemistry 

N2O5 Reaction Probability None 

Meteorological Processor MM5CAMx 

Horizontal Transport 

Eddy Diffusivity Scheme 

K-theory with Kh grid size 

dependence 

Vertical Transport 

Eddy Diffusivity Scheme K-Theory 

Diffusivity Lower Limit Kzmin = 0.1 to 1.0 Land use dependent Kzmin 

Planetary Boundary Layer No Patch 

Deposition Scheme Wesely 

Numerics 

Gas Phase Chemistry Solver CMC Fast Solver 

Horizontal Advection Scheme 

Piecewise Parabolic Method 

(PPM) scheme. PSAT w/ 

Bott scheme. 

Simulation Periods Annual 2002 at 36 km 

Integration Time Step Wind speed dependent 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-13 

1.3.4 Modeling Domains 

The CENRAP emissions and air quality modeling was conducted on the 36 km national RPO 

domain as depicted in Figure 1-2. This domain consists of a 148 by 112 array of 36 km by 36 

km grid cells and covers the continental United States. Sensitivity simulations were also 

performed for episodes on a 12 km modeling domain covering the central states, however the 

results were very similar to the 36 km results so CENRAP elected to proceed with the 2002 

annual modeling using the 36 km domain for computational efficiency (Morris et al., 2006a). 

-2736 -2304 -1872 -1440 -1008 -576 -144 288 720 1152 1584 2016 2448

-2088

-1872

-1656

-1440

-1224

-1008

-792

-576

-360

-144

72

288

504

720

936

1152

1368

1584

1800

Figure 1-2. National Inter-RPO 36 km modeling domain used for the CENRAP 2002 annual 

SMOKE, CMAQ and CAMx modeling. 

1.3.5 Vertical Structure of Modeling Domain 

The MM5 meteorological model was exercised using 34 vertical layers from the surface to a 

pressure level of 100 mb (approximately 15 km above ground level). Both the CMAQ and 

CAMx air quality models can employ layer collapsing in which vertical layers in the MM5 are 

combined in the air quality model, which improves computational efficiency. The sensitivity of 

the CMAQ model estimates to the number of vertical layers was evaluated by the Western 

Regional Air Partnership (WRAP) and Visibility Improvements State and Tribal Association of 

the Southeast (VISTAS) (Tonnesen et al., 2005; 2006; Morris et al., 2004a). CMAQ model 

simulations were performed with no layer collapsing (i.e., the same 34 layers as used by MM5) 

and with various levels of layer collapsing. These studies found that using 19 vertical layers up 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-14 

to 100 mb (i.e., same model top as MM5) and matching the eight lowest MM5 vertical layers 

near the surface produced nearly identical results as with no layer collapsing. They also found 

that very aggressive layer collapsing (e.g., 34 to 12 layers) produced results with substantial 

differences compared to no layer collapsing. Therefore, based on the WRAP/VISTAS sensitivity 

analysis, CENRAP adopted the 19 vertical layer configuration up to the 100 mb model top. 

Figure 1-3 displays the definition of the 34 MM5 vertical layers and how they were collapsed to 

19 vertical layers in the air quality modeling performed by CENRAP. 

Figure 1-3. MM5 34 vertical layer definitions and scheme for collapsing the 34 layers down to 19 

layers for the CENRAP CMAQ and CAMx 2002 annual modeling. 

MM5 CMAQ 19L

Layer Sigma Pres(mb) Height(m Depth(m) Layer Sigma Pres(mb) Height(m) Depth(m)

34 0.000 100 14662 1841 19 0.000 100 14662 6536

33 0.050 145 12822 1466 0.050 145

32 0.100 190 11356 1228 0.100 190

31 0.150 235 10127 1062 0.150 235

30 0.200 280 9066 939 0.200 280

29 0.250 325 8127 843 18 0.250 325 8127 2966

28 0.300 370 7284 767 0.300 370

27 0.350 415 6517 704 0.350 415

26 0.400 460 5812 652 0.400 460

25 0.450 505 5160 607 17 0.450 505 5160 1712

24 0.500 550 4553 569 0.500 550

23 0.550 595 3984 536 0.550 595

22 0.600 640 3448 506 16 0.600 640 3448 986

21 0.650 685 2942 480 0.650 685

20 0.700 730 2462 367 15 0.700 730 2462 633

19 0.740 766 2095 266 0.740 766

18 0.770 793 1828 259 14 0.770 793 1828 428

17 0.800 820 1569 169 0.800 820

16 0.820 838 1400 166 13 0.820 838 1400 329

15 0.840 856 1235 163 0.840 856

14 0.860 874 1071 160 12 0.860 874 1071 160

13 0.880 892 911 158 11 0.880 892 911 158

12 0.900 910 753 78 10 0.900 910 753 155

11 0.910 919 675 77 0.910 919

10 0.920 928 598 77 9 0.920 928 598 153

9 0.930 937 521 76 0.930 937

8 0.940 946 445 76 8 0.940 946 445 76

7 0.950 955 369 75 7 0.950 955 369 75

6 0.960 964 294 74 6 0.960 964 294 74

5 0.970 973 220 74 5 0.970 973 220 74

4 0.980 982 146 37 4 0.980 982 146 37

3 0.985 986.5 109 37 3 0.985 986.5 109 37

2 0.990 991 73 36 2 0.990 991 73 36

1 0.995 995.5 36 36 1 0.995 995.5 36 36

0 1.000 1000 0 0 0 0 1.000 1000 0 0
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-15 

1.3.6 2002 Calendar Year Selection 

The calendar year 2002 was selected for CENRAP regional haze annual modeling as described 

in the CENRAP Modeling Protocol (Morris et al., 2004a). EPA’s applicable guidance on 

PM2.5/Regional Haze modeling at that time (EPA, 2001) identified specific goals to consider 

when selecting modeling periods for use in demonstrating reasonable progress in attaining the 

regional haze goals. However, since there is much in common with the goals for selecting 

episodes for annual and episodic PM2.5 attainment demonstrations as well as regional haze, 

EPA’s current guidance addresses all three in a common document. (EPA, 2007) At the time of 

the modeling period selection EPA had also published an updated summary of PM2.5 and 

Regional Haze Modeling Guidance (Timin, 2002) that served, in some respects, as an interim 

placeholder until the final guidance was issued as part of the PM2.5/regional haze NAAQS 

implementation process that was ultimately published in April 2007 (EPA, 2007). The interim 

EPA modeling guidance for episode selection (EPA, 2001; Timin, 2002) was consistent with the 

final EPA regional haze modeling guidance (EPA, 2007). 

EPA recommends that the selection of a modeling period derive from three principal criteria: 

3⁄4 A variety of meteorological conditions should be covered that includes the types of 

meteorological conditions that produce the worst 20 percent and best 20 percent visibility 

days at Class I areas in the CENRAP States during the 2000-2004 baseline period; 

 

3⁄4 To the extent possible, the modeling data base should include days for which enhanced 

data bases (i.e. beyond routine aerometric and emissions monitoring) are available; and 

3⁄4 Sufficient days should be available such that relative response factors (RRFs) can be 

based on several (i.e., > 15) days. 

 

For regional haze modeling, the guidance goes further by suggesting that the preferred approach 

is to model a full, representative year (EPA, 2001, pg. 188). Moreover, the required RRF values 

should be based on model results averaged over the 20 percent worst and 20 percent best 

visibility days determined for each Class I area based on monitoring data from the 2000 – 2004 

baseline period. More recent EPA guidance (Timin, 2002) suggests that states should model at 

least 10 worst and 10 best visibility days at each Class 1 area. EPA also lists several ‘other 

considerations’ to bear in mind when choosing potential PM/regional haze episodes including: 

(a) choose periods which have already been modeled, (b) choose periods which are drawn from 

the years upon which the current design values are based, (c) include weekend days among those 

chosen, and (d) choose modeling periods that meet as many episode selection criteria as possible 

in the maximum number of nonattainment or Class I areas as possible. 

Due to limited available resources CENRAP was restricted to modeling a single calendar year. 

The RHR uses the five-year baseline of 2000-2004 period as the starting point for projecting 

future-year visibility. Thus, the modeling year should be selected from this five-year baseline 

period. The 2002 calendar year, which lies in the middle of the 2000-2004 Baseline, was 

selected for the following reasons: 

3⁄4 Based on available information, 2002 appears to be a fairly typical year in terms of 

meteorology for the 5-year Baseline period of 2000-2004; 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-16 

3⁄4 2003 and 2004 appeared to be colder and wetter than typical in the eastern US; 

3⁄4 The enhanced IMPROVE and IMPROVE Protocol and Supersites PM monitoring data 

were fully operational by 2002. Much less IMPROVE monitoring data was available 

during 2000-2001, especially in the CENRAP region; 

3⁄4 IMPROVE data for 2003 and 2004 were not yet available at the time that the CENRAP 

modeling was initiated; and 

3⁄4 2002 was being used by the other RPOs. 

1.3.7 Initial Concentrations and Boundary Conditions 

The CMAQ and CAMx models were operated separately for each of four quarters of the 2002 

year using a ~15 day spin up period (i.e., the models were started approximately 15 days before 

the first day of interest in each quarter in order to limit the influence of the assumed initial 

concentrations, e.g., start June 15 for quarter 3 whose first day of interest is July 1). Sensitivity 

simulations demonstrated that with ~15 initialization days, the influence of initial concentrations 

(ICs) was minimal using the 36 km Inter-RPO continental U.S. modeling domain. 

Consequently, clean ICs were specified in the CMAQ and CAMx modeling using a ~15 day spin 

up period. 

Boundary Conditions (BCs) (i.e., the assumed concentrations along the later edges of the 36 km 

modeling domain, see Figure 1-2) were based on a 2002 simulation by the GEOS-CHEM global 

circulation/chemistry model. GEOS-CHEM is a three-dimensional global chemistry model 

driven by assimilated meteorological observations from the Goddard Earth Observing System 

(GEOS) of the NASA Global Modeling and Assimilation Office. It is applied by research groups 

around the world to a wide range of atmospheric composition problems, including future 

climates and planetary atmospheres using general circulation model meteorology to drive the 

model. Central management and support of the model is provided by the Atmospheric Chemistry 

Modeling Group at Harvard University. 

A joint RPO study was performed, coordinated by VISTAS, in which Harvard University 

applied the GEOS-CHEM global model for the 2002 calendar year (Jacob, Park and Logan, 

2005). The University of Houston (UH) was retained to process the 2002 GEOS-CHEM output 

into BCs for the CMAQ model (Byun, 2004). The GEOS-CHEM simulations for the RPOs used 

GEOS meteorological observations for the year 2002. These were obtained from the Global 

Modeling and Assimilation Office(GMAO) as a 6-hourly archive (3-hour for surface quantities 

such as mixing depths). The data through August 2002 were from the GEOS-3 assimilation, 

with horizontal resolution of 1o

x1o and 55 vertical layers. The data after August 2002 were from 

the updated GEOS-4 assimilation, with horizontal resolution of 1o

x1.25o and 48 vertical layers 

(note 1o latitude is equal to approximately 110 km). The GEOS-CHEM output was processed by 

mapping the GEOS-CHEM chemical compounds to the species in the CBM-IV chemical 

mechanism used by CMAQ/CAMx and mapping the GEOS-CHEM vertical layers to the 19 

layer vertical layer structure used by CMAQ/CAMx in the CENRAP modeling (Byun, 2004). 

The results were day-specific three-hourly BC inputs for the CMAQ model. The CMAQ2CAMx 

processor was then used to transform the CMAQ day-specific 3-hourly BCs to the format used 

by CAMx. 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-17 

There were several quality assurance (QA) checks of the BCs generated from the 2002 GEOSCHEM output. The first QA/QC check was a range check to assure reasonable values. The BCs 

were compared against the GEOS-CHEM outputs to assure the mapping and interpolation was 

performed correctly. The code used to map the GEOS-CHEM output to the CMAQ BC format 

was obtained from UH, reviewed and the BC generation duplicated for several time periods 

during 2002. 

1.3.8 Emissions Input Preparation 

The CENRAP SMOKE emissions modeling was based on an updated 2002 emissions data for 

the U.S. (Pechan, 2005c,e; Reid et al., 2004a,b), 1999 emissions data for Mexico (ERG, 2006), 

and 2000 emissions data for Canada. These data were used to generate a final base 2002 Base G 

Typical (Typ02G) annual emissions database. Numerous iterations of the emissions modeling 

were conducted using interim databases before arriving at the final Base G emission inventories 

(e.g., Morris et al., 2005). The 2018 Base G base case emissions (Base18G) for most source 

categories in the U.S. were based on projections of the 2002 inventory assuming growth and 

control (Pechan, 2005d). 2018 EGU emissions were based on the run 2.1.9 of the Integrated 

Planning Model (IPM) updated by the CENRAP states. Canadian emissions for the Base18G 

scenario were based on a 2020 inventory, whereas the Mexican 1999 inventory was held 

constant for 2018. 

The Typ02G and Base18G emission inventories represent significant improvements to the 

preliminary emissions modeling performed by CENRAP (Morris et al., 2005). While the 

preliminary 2002 modeling served its purpose to develop the infrastructure for modeling large 

emissions data sets and producing annual emissions simulations, much of the input data (both as 

inventories and ancillary data) were placeholders for actual 2002 data that were being prepared 

through calendar year 2005. As these actual 2002 data sets became available, they were 

integrated into the SMOKE modeling and QA system that was developed during the preliminary 

modeling, to produce a high-quality emissions data set for use in the final CMAQ and CAMx 

modeling. The addition of entirely new inventory categories, like marine shipping, added 

complexity to the modeling. By the end of the emissions data collection phase, there were 23 

separate emissions processing streams covering a variety of sources categories necessary to 

general model-ready emission inputs for the 2002 calendar year. 

Details on the emissions modeling are provided in Chapter 2 with additional information 

contained in Appendix B. 

1.3.9 Meteorological Input Preparation 

The 2002 36 km MM5 meteorological modeling was conducted by the Iowa Department of 

Natural Resources (IDNR) who also performed a preliminary model performance evaluation 

(Johnson, 2007). CENRAP performed an additional MM5 evaluation of the CENRAP 2002 36 

km MM5 simulation that included a comparative evaluation against the final VISTAS 2002 36 

km MM5 and an interim WRAP 2002 36 km simulation (Kemball-Cook et al., 2004). KemballCook and co-workers (2004) found the following in the comparative evaluation of the CENRAP, 

WRAP and VISTAS 2002 36 km MM5 simulations, (details are provided in Appendix A): 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-18 

Surface Meteorological Performance within the CENRAP Region

• The three MM5 simulations (CENRAP, VISTAS and WRAP) obtained comparable 

model performance for winds and humidity that were within model performance 

benchmarks. 

• The WRAP MM5 simulation obtained better temperature model performance than the 

other two simulations due to the use of surface temperature data assimilation. 

o In the final WRAP MM5 simulation the use of surface temperature assimilation 

was dropped because it introduced instability in the vertical structure of the 

atmosphere. 

• For all three runs, the Northern CENRAP domain had a cold bias in winter and a warm 

bias in summer. 

Surface Meteorological Performance outside the CENRAP Region

• All three runs had similar surface wind model performance in the western U.S. that was 

outside the model performance benchmarks 

• For temperature, the WRAP MM5 simulation had the best performance overall due to the 

surface temperature data assimilation that was dropped in the final WRAP run. 

• The three runs had comparable humidity performance, although WRAP exhibited a larger 

wet bias in the summer and the southwestern U.S. 

Upper-Air Meteorological Performance

• The VISTAS and CENRAP MM5 simulations were better able to reproduce the deep 

convective summer boundary layers compared to the WRAP MM5 simulations, which 

exhibited a smoother decrease in temperature with increase in altitude. 

• CENRAP and VISTAS MM5 simulations better simulated the surface temperature 

inversions than WRAP. 

• WRAP was better able to simulate the surface temperature. 

• All three models exhibited similar vertical wind profiles. 

Precipitation Performance

• In winter, all three MM5 simulations exhibited similar, fairly good, performance in 

reproducing the spatial distribution and magnitudes of the monthly average observed 

precipitation. 

• In summer, all runs had a wet bias, particularly in the desert southwest where the interim 

WRAP run had the largest wet bias. 

In conclusion, the VISTAS simulation appeared to perform best, the CENRAP MM5 model 

performance was generally between the VISTAS and WRAP performance, with performance 

more similar to VISTAS than WRAP. Although the interim WRAP MM5 simulation performed 

best for surface temperature due to the surface temperature data assimilation, the surface 

temperature assimilation degraded the MM5 upper-air performance including the ability to 

assimilate surface inversions and was ultimately dropped from the final WRAP MM5 

simulations (Kemball-Cook et al., 2005). 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-19 

The IDNR 12 km2 MM5 simulations were also evaluated and compared with the performance of 

the 36 km MM5 simulation (Johnson et al., 2007). The IDNR 36 km and 12 km MM5 model 

performance was similar (Johnson, 2007), which supported the findings of the CMAQ and 

CAMx 36 and 12 km sensitivity simulations that there was little benefit of using a 12 km grid for 

simulating regional haze at rural Class I areas (Morris et al., 2006a). However, as noted by 

Tonnesen and co-workers (2005; 2006) and EPA modeling guidance (1991; 1999; 2001; 2007) 

this finding does not necessarily hold for 8-hour ozone and PM2.5 modeling that is characterized 

by sharper concentration gradients and frequently occurs in the urban environment as compared 

to the more rural nature of regional haze. 

1.3.10 Photolysis Rates Model Inputs 

Several chemical reactions in the atmosphere are initiated by the photodissociation of various 

trace gases. To accurately represent the complex chemical transformations in the atmosphere, 

accurate estimates of these photodissociation rates must be made. The Models-3/CMAQ system 

includes the JPROC processor, which calculates a table of clear-sky photolysis rates (or J-values) 

for a specific date. JPROC uses default values for total aerosol loading and provides the option to 

use default ozone column data or to use measured total ozone column data. These data come 

from the Total Ozone Mapping Spectrometer (TOMS) satellite data. TOMS data that is available 

at 24-hour averages was obtained from http://toms.gsfc.nasa.gov/eptoms/ep.html. Day-specific 

TOMS data was used in the CMAQ radiation model (JPROC) to calculate photolysis rates. The 

TOMS data were missing or erroneous for several periods in 2002: August 2-12; June 10; and 

November 18-19. Thus, the TOMS data for August 1, 2002 was used for August 2-7 and TOMS 

data for August 13 was used for August 8-12. Similarly, TOMS data for June 9 was used for 

June 10 and data for August 17 was used for August 18-19. Note that the total column of ozone 

in the atmosphere is dominated by stratospheric ozone which has very little day-to-day 

variability so the use of TOMS data within a week or two of an actual day introduces minimal 

uncertainties in the modeling analysis. 

JPROC produces a "look-up" table that provides photolysis rates as a function of latitude, 

altitude, and time (in terms of the number of hours of deviation from local noon, or hour angle). 

In the current CMAQ implementation, the J-values are calculated for six latitudinal bands (10o, 

20o, 30o, 40o, 50o, and 60o N), seven altitudes (0 km, 1 km, 2 km, 3 km, 4 km, 5 km, and 10 km), 

and hourly values up to ∀8 hours of deviation from local noon. During model calculations, 

photolysis rates for each model grid cell are estimated by first interpolating the clear-sky 

photolysis rates from the look-up table using the grid cell latitude, altitude, and hour angle, 

followed by applying a cloud correction (attenuation) factor based on the cloud inputs from 

MM5. 

The photolysis rates input file was prepared as separate look-up tables for each simulation day. 

Photolysis files are ASCII files that were visually checked for selected days to verify that 

photolysis are within the expected ranges. 

 

2 The IDNR twelve 12 km annual simulation domain was not sufficient for CENRAP’s needs, thus Bret Anderson 

with EPA Region 7 in cooperation with Texas completed an episodic 12km simulation on a larger domain.
 

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The Tropospheric Ultraviolet and Visible (TUV) Radiation Model 

(http://cprm.acd.ucar.edu/Models/TUV/) is used to generate the photolysis rates input file for 

CAMx. TOMS ozone data and land use data were used to develop the CAMx 

Albedo/Haze/Ozone input file for 2002. As for CMAQ, the missing TOMS data period in the 

fall of 2002 was filled-in using observed TOMS data on either side of the missing period using 

the same procedures as described above for CMAQ. Default land use specific albedo values 

were used and a constant haze value used, corresponding to rural conditions over North America.

1.3.11 Air Quality Input Preparation 

Air quality data used with the CMAQ and CAMx modeling systems include: (1) Initial 

Concentrations (ICs) that are the assumed initial three-dimensional concentrations throughout the 

modeling domain.; (2) the Boundary Conditions (BCs) that are the concentrations assumed along 

the lateral edges of the RPO national 36 km modeling domain; and (3) air quality observations 

that are used in the model performance evaluation (MPE). The MPE is discussed in Section 3 

and Appendix C of this TSD. 

As noted in Section 1.3.7, CMAQ default clean Initial Concentrations (ICs) were used along 

with an approximately 15 day spin up (initialization) period to eliminate any significant 

influence of the ICs on the modeled concentrations for the days of interest. The same ICs were 

used with CAMx as well. Both CMAQ and CAMx were run for each quarter of the year. Each 

quarter’s model run was initialized 15 days prior to the first day of interest (e.g., for quarter 3, 

Jul-Aug-Sep, the model was initialized on June 15, 2002 with the first modeling day of interest 

July 1, 2002). The CMAQ Boundary Conditions (BCs) for the Inter-RPO 36 km continental 

U.S. grid (Figure 1-2) were based on day-specific 3-hour averages from the output of the GEOSCHEM global simulation model of 2002 (Jacob, Park and Logan, 2005). The 2002 GEOSCHEM output was mapped to the species and vertical layer structure of CMAQ and interpolated 

to the lateral boundaries of the 36 km grid shown in Figure 1-2 (Byun, 2004). 

Table 1-6 summarizes the surface air quality monitoring networks and the number of sites 

available in the CENRAP region that were used in the model performance evaluation. Data from 

these monitoring networks were also used to evaluate the CMAQ and CAMx models outside of 

the CENRAP region. 

Table 1-6. Ground-level ambient data monitoring networks and stations available in the 

CENRAP states for calendar year 2002 used in the model performance evaluation. 

Monitoring 

Network Chemical Species Measured 

Sampling 

Frequency; 

Duration 

Approximate 

Number of 

Monitors 

IMPROVE Speciated PM2.5 and PM10 1 in 3 days; 24 hr 11 

CASTNET Speciated PM2.5, Ozone Hourly, Weekly; 

1 hr, 1 Week 

3 

NADP WSO4, WNO3, WNH4 Weekly 23 

EPA-STN Speciated PM2.5 Varies; Varies 12 

AIRS/AQS CO, NO, NO2, NOx, O3 Hourly; Hourly 25 
 

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1.3.12 2002 Base Case Modeling and Model Performance Evaluation 

The CMAQ and CAMx models were evaluated against ambient measurements of PM species, 

gas-phase species and wet deposition. Table 1-6 summarizes the networks used in the model 

evaluation, the species measured and the averaging times and frequency of the measurements. 

Numerous iterations of CMAQ and CAMx 2002 base case simulations and model performance 

evaluations were conducted during the course of the CENRAP modeling study, most of which 

have been posted on the CENRAP modeling website 

(http://pah.cert.ucr.edu/aqm/cenrap/cmaq.shtml) and presented in previous reports and 

presentations for CENRAP (e.g., Morris et al., 2005; 2006a,b). Details on the final 2002 Base F 

36 km CMAQ base case modeling performance evaluation are provided in Chapter 3 and 

Appendix C (because of the similarity between 2002 Base F and 2002 Base G and resource 

constraints the model evaluation was not re-conducted for Base G). In general, the model 

performance of the CMAQ and CAMx models for sulfate (SO4) and elemental carbon (EC) was 

good. Model performance for nitrate (NO3) was variable, with a summer underestimation and 

winter overestimation bias. Performance for organic mass carbon (OMC) was also variable, with 

the inclusion of the SOAmods enhancement in CMAQ Version 4.5 greatly improving the CMAQ 

summer OMC model performance (Morris et al., 2006c). Model performance for Soil and 

coarse mass (CM) was generally poor. Part of the poor performance for Soil and CM is believed 

to be due to measurement-model incommensurability. The IMPROVE measured values are due, 

in part, to local fugitive dust sources that are not captured in the model’s emission inputs and the 

36 km grid resolution is not conducive to modeling localized events. 

 

1.3.13 2018 Modeling and Visibility Projections 

Emissions for the 2018 base case were generated following the procedures discussed in Section 

1.3.8 and Chapter 2. 2018 emissions for Electrical Generating Units (EGUs) were based on 

simulations of the Integrated Planning Model (IPM) that took into the account the effects of the 

Clean Air Interstate Rule (CAIR) on emissions from EGUs in CAIR states using an IPM 

realization of a CAIR cap-and-trade program. Emissions for on-road and non-road mobile 

sources were based on activity growth and emissions factors from the EPA MOBILE6 and 

NONROAD models, respectively. Area sources and non-EGU point sources were grown to 

2018 levels (Pechan, 2005d). The Canadian year 2000 emissions inventory was replaced by a 

Canadian 2020 emissions inventory for the 2018 CMAQ/CAMx simulations. The following 

sources were assumed to remain constant between the 2002 and 2018 base case simulations: 

• Biogenic VOC and NOx emissions from the BEIS3 biogenic emissions model; 

• Wind blown dust associated with non-agricultural sources (i.e., natural wind blown 

fugitive dust); 

• Off-shore emissions associated with off-shore marine and oil and gas production 

activities; 

• Emissions from wildfires; 

• Emissions from Mexico; and 

• Global transport (i.e., emissions due to BCs from the 2002 GEOS-CHEM global 

chemistry model. 
 

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The results from the 2002 and 2018 CMAQ and CAMx simulations were used to project 2018 

PM levels from which 2018 visibility estimates were obtained. The 2002 and 2018 modeling 

results were used in a relative sense to scale the observed PM concentrations from the 2000-2004 

Baseline and the IMPROVE monitoring network to obtain the 2018 PM projections. The 

2018/2002 modeled scaling factors are called Relative Response Factors (RRFs) and are 

constructed as the ratio of modeling results for the 2018 model simulation to the 2002 model 

simulation. Two important regional haze metrics are the average visibility for the worst 20 

percent and best 20 percent days from the 2000-2004 five-year Baseline. For the 2018 visibility 

projections, EPA guidance recommends developing Class I area and PM species specific RRFs 

using the average modeling results for the worst 20 percent days during the 2002 modeling 

period and the 2002 and 2018 emission scenarios. The results of the CENRAP 2018 visibility 

projections following EPA guidance procedures (EPA, 2007a) are provided in Chapter 4 and 

Appendix D. CENRAP has also developed alternative procedures for visibility projections that 

are discussed in Chapter 5 and Appendix D. For example, much of the coarse mass (CM) 

impacts at Class I area IMPROVE monitors is believed to be natural and primarily from local 

sources that are subgrid-scale to the modeled 36 km grid so are not represented in the modeling. 

So, one alternative visibility projection approach is to set the RRF for CM to 1.0. That is, the CM 

impacts in 2018 are assumed to be the same as in the observed 2000-2004 Baseline. Similarly, 

the Soil impacts at IMPROVE monitors are likely mainly due to local dust sources so another 

alternative approach is to set the RRFs for both CM and Soil to 1.0. 

The 2018 visibility projections for the worst 20 percent days are compared against a 2018 point 

on the Uniform Rate of Progress (URP) glidepath or the “2018 URP point”. The 2018 URP 

point is obtained by constructing a linear visibility glidepath in deciviews from the observed 

2000-2004 Baseline (EPA, 2003a) for the worst 20 percent days to the 2064 Natural Conditions 

(EPA, 2003b; Pitchford, 2006). Where the linear glidepath crosses the year 2018 is the 2018 

URP point. States may use the modeled 2018 visibility to help define their 2018 RPG in their 

RHR SIPs. The 2018 URP point is used as a benchmark to help judge the 2018 modeled 

visibility projections and the state’s RPG. However, as noted in EPA’s RPG guidance “The 

glidepath is not a presumptive target, and States may establish a RPG that provides for greater, 

lesser, or equivalent visibility improvement as that described by the glidepath” (EPA, 2007b). 

Chapter 4 and Appendix D present the 2018 visibility projections for the CENRAP Class I areas 

and their comparisons with the 2018 URP point using EPA default visibility projection 

procedures (EPA, 2007a) and EPA default URP glidepaths (EPA, 2003a,b; 2007b). 

Various techniques have been developed to display the 2018 visibility modeling results including 

“DotPlots” that display the 2018 visibility projections as a percentage of meeting the 2018 point 

on the URP glidepath. A value of 100% on the DotPlot indicates that the Class I area is predicted 

to meet the 2018 point on the URP glidepath. Over 100% means the 2018 visibility projection 

obtains more visibility improvements (reductions) than required to meet the 2018 point on the 

URP glidepath (i.e., projected value is below the glidepath). And less than 100% indicates that 

fewer visibility improvements are projected than are needed to meet the 2018 point URP on the 

glidepath (i.e., above the glidepath). Figure 1-4 displays a DotPlot that compares the 2018 

visibility projections from the CENRAP 2018 Base G CMAQ simulation with the 2018 URP 

point using the EPA default RRFs and alternative RRFs that set the CM and Soil RRFs to unity 

(i.e., assume CM and Soil are natural so remain unchanged from the 2000-2004 Baseline). For 

these results, the 2018 visibility projections at the Hercules Glade (HEGL1) Class I area meets 

the 2018 point on the URP glidepath (100%), whereas the 2018 visibility projections at Caney 
 

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Creek (CACR), Mingo (MING) and Upper Buffalo (UPBU) achieve more visibility 

improvements than needed to meet the 2018 URP point so are below the 2018 URP glidepath. 

However, the 2018 visibility projections at Breton Island comes up slightly short (~5%) of 

meeting the 2018 point on the URP glidepath and Wichita Mountains (WIMO) comes up 

approximately 40% short of meeting the 2018 point on the URP glidepath. Class I areas at the 

northern (e.g., VOYA, BOWA and ISLE) and southern (e.g., BIBE and GUMO) boundaries of 

the U.S. also fall short of achieving the 2018 URP point. High contributions of international 

transport and/or natural sources (e.g., wind blown dust) affect the ability of these Class I areas to 

be on the URP glidepath. These issues are discussed in more detail in Chapters 4 and 5. 

CMAQ BaseGa Method 1 predictions for CENRAP+ sites

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

BIBE1

GUMO1

WIMO1

CACR1

UPBU1

HEGL1

MING1

BRET1

VOYA2

BOWA1

MACA1

SIPS1

ISLE1

SACR1

WHIT1

WHPE1

GRSA1

ROMO1

WICA1

BADL1

THRO1

LOST1

Percent of target reduction achieved

Regular RRF

CM RRF=1

CM&SOIL RRFs=1

CENRAP non-CENRAP

Figure 1-4. 2018 visibility projections expressed as a percent of meeting the 2018 URP point 

for the 2018 BaseG CMAQ base case simulation using the EPA default (EPA, 2007) Regular 

RRF and alternative projections procedures that set the RRFs for CM=1.0 and CM&SOIL=1.0. 

 
 

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1.3.14 Additional Supporting Analysis 

CENRAP performed numerous supporting analyses of its modeling results including analyzing 

alternative glidepaths and 2018 projection Approaches and performing confirmatory analysis of 

the 2018 visibility projections. Details on the additional supporting analysis are contained 

discussed in Chapter 5, which include: 

• The CENRAP 2018 visibility projections were compared with those generated by 

VISTAS and MRPO. There was close agreement between the CENRAP and VISTAS 

2018 visibility projections at almost all common Class I areas. With the only exception 

being Breton Island where the CENRAP’s projections were slightly more optimistic than 

VISTAS’. The MRPO 2018 visibility projections were less optimistic than CENRAP’s 

at the four Arkansas-Missouri Class I area that may have been due to CENRAP’s BART 

emission controls in CENRAP states not included in the 2018 MRPO inventory. 

• Extinction based glidepaths were developed and the CENRAP 2018 visibility projections 

were shown to produce nearly identical estimates of achieving the 2018 URP point when 

using total extinction glidepaths as when the linear deciview glidepaths were used. With 

the extinction based glidepaths the analysis of 2018 URP could be made on a PM 

species-by-species basis where it was shown that 2018 extinctions due to SO4 and, to a 

lesser extent, NO3 and EC, achieve the URP, but the other species do not and in fact 

extinction due to Soil and CM is projected to get worse. 

• 2018 visibility projections were made using EPA’s new Modeled Attainment Test 

Software (MATS) program and the CENRAP Typ02G and Base18G modeling results. 

The CENRAP 2018 visibility projections exactly agreed with those generated by MATS 

with three exceptions: Breton Island, Boundary Waters and Mingo Class I areas, At these 

three Class I areas MATS did not produce any 2018 visibility projections due to 

insufficient data in the raw IMPROVE database to produce a valid observed 2000-2004 

Baseline. CENRAP used filled data for these three Class I areas. 

• PM Source Apportionment Technology (PSAT) modeling was conducted to estimate the 

contributions to visibility impairment at Class I areas by source region (e.g., states) and 

major source category. Source contributions were obtained for a 2002 and 2018 base 

case and the PSAT modeling results were implemented in a PSAT Visualization Tool 

that was provided to CENRAP states and others. Major findings from the PSAT source 

apportionment modeling include the following: 

o Sulfate from elevated point sources was the highest source category contribution 

to visibility impairment at CENRAP Class I areas for the worst 20 percent days. 

o International transport contributed significantly to visibility impairment at 

CENRAP Class I areas on the southern (BIBE and GUMO) and northern (BOWA 

and VOYA) borders of the U.S. and to a lesser extent at WIMO as well. 

• Alternative visibility projections were made assuming that coarse mass (CM) alone and 

CM and Soil were natural in origin that confirmed the original 2018 visibility projections. 

• Visibility projections were made using an alternative model (CAMx) that verified the 

projections made by CMAQ. 

• The effects of International Transport were examined several ways and found that the 

inability of the 2018 visibility projections to achieve the 2018 URP point at the northern 

and southern border Class I areas was due to high contributions due to International 

Transport. 
 

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• Visibility trends for the worst 20 percent days, best 20 percent days and all monitored 

days were analyzed at CENRAP Class I areas using the period of record IMPROVE 

observations. At most Class I areas there was insufficient years of data to produce a 

discernable trend. In addition, there was significant year-to-year variability in visibility 

impairment with episodic events (e.g., wildfires and wind blown dust) confounding the 

analysis. 

1.4 Organization of the Report 

Chapter 1 of this TSD presents background, an overview of the approach and summary of the 

results of the CENRAP meteorological, emissions and air quality modeling. Appendix A 

contains more details on the meteorological model evaluation discussed in Chapter 1. Details on 

the emissions modeling are provided in Chapter 2 and Appendix B. The model performance 

evaluation is given in Chapter 3 and Appendix C. The 2018 visibility projections and 

comparisons with the 2018 URP point are provided in Chapter 4 with more details given in 

Appendix D. Chapter 5 contains additional supporting analysis with details on the PM source 

apportionment modeling and alternative projections provided in Appendices E and F, 

respectively. Chapter 6 lists the references cited in the report. 
 

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2.0 EMISSIONS MODELING 

2.1 Emissions Modeling Overview 

For the emissions modeling work conducted in support of CENRAP air quality modeling, we 

used updated 2002 emissions data for the U.S., 1999 emissions data for Mexico, and 2000 

emissions data for Canada to generate a final base 2002 Base G Typical (Typ02G) annual 

emissions database. Numerous iterations of the emissions modeling were conducted using 

interim databases before arriving at the final Base G emission inventories. The 2002 and 2018 

emissions inventories and ancillary modeling data were provided by CENRAP emissions 

inventory contractors (Pechan and CEP, 2005c,e; Reid et al., 2004a,b; Coe and Reid, 2003), 

other Regional Planning Organizations (RPOs) and EPA. Building from the CENRAP 

preliminary 2002 database (Pechan and CEP. 2005e) and 2018 projections (Pechan, 2005d), we 

integrated several updates to the inventories and ancillary data to create final emissions input 

files; the final simulations are referred to as 2002 Typical and 2018 Base G, or Typ02G and 

Base18G. We used the Sparse Matrix Operator Kernel Emissions (SMOKE) version 2.1 

processing system (CEP, 2004) to prepare the inventories for input to the air quality modeling 

systems. The SMOKE simulations documented in this report include emissions generated for 

annual CMAQ and CAMx simulations at a 36-km model grid resolution, and a short-term 

CMAQ test simulation at a 12-km model grid resolution. We performed the modeling and 

quality assurance (QA) work based on the CENRAP modeling Quality Assurance Project Plan 

(QAPP; Morris and Tonnesen, 2004) and Modeling Protocol (Morris et al., 2004a). 

The Typ02G and Base18G emission inventories represent significant improvements to the 

preliminary emissions modeling performed by CENRAP (Morris et al., 2005). While the 

preliminary 2002 modeling served its purpose to develop the infrastructure for modeling large 

emissions data sets and producing annual emissions simulations, much of the input data (both as 

inventories and ancillary data) were placeholders for actual 2002 data that were being prepared 

through calendar year 2005. As these actual 2002 data sets became available, they were 

integrated into the SMOKE modeling and QA system that was developed during the preliminary 

modeling, to produce a high-quality emissions data set for use in the final CMAQ and CAMx 

modeling. The addition of entirely new inventory categories, like marine shipping, added 

complexity to the modeling. By the end of the emissions data collection phase, there were 23 

separate emissions processing streams covering a variety of sources categories necessary to 

general model-ready emission inputs for the 2002 calendar year. 

2.1.1 SMOKE Emissions Modeling System Background 

The purpose of SMOKE (or any emissions processor) is to process the raw emissions reported by 

states and EPA into gridded hourly speciated emissions required by the air quality model. 

Emission inventories are typically available as an annual total emissions value for each 

emissions source, or perhaps with an average-day emissions value. The air quality models, 

however, typically require emissions data on an hourly basis, for each model grid cell (and 

perhaps model layer), and for each model species. Consequently, emissions processing involves 

(at a minimum) transformation of emission inventory data by temporal allocation, chemical 

speciation, spatial allocation, and perhaps layer assignment, to achieve the input requirements of 

the air quality model. For the CENRAP modeling effort, all of these steps were needed. In 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_2_Emissions1.doc 2-2 

addition, CENRAP processing requires special MOBILE6 processing and growth and control of 

emissions for the future-year inventories. Finally, the biogenic emission processing using BEIS2 

includes additional processing steps. SMOKE formulates emissions modeling in terms of sparse 

matrix operations. Figure 2-1 shows an example of how the matrix approach organizes the 

emissions processing steps for anthropogenic emissions, with the final step that creates the 

model-ready emissions being the merging of all the different processing streams of emissions 

into a total emissions input file for the air quality model. Figure 2-1 does not include all the 

potential processing steps, which can be different for each source category in SMOKE, but does 

include the major processing steps listed in the previous paragraph, except the layer assignment. 

Specifically, the inventory emissions are arranged as a vector of emissions, with associated 

vectors that include characteristics about the sources such as its state and county or source 

classification code (SCC). SMOKE also creates matrices that will apply the gridding, speciation, 

and temporal factors to the vector of emissions. In many cases, these matrices are independent 

from one another, and can therefore be generated in parallel. The processing approach ends with 

the merge step, which combines the inventory emissions vector (now an hourly inventory file) 

with the control, speciation, and gridding matrices to create model-ready emissions. 

Figure 2-1. Flow diagram of major SMOKE processing steps needed by all source categories.

Temporal processing includes both seasonal or monthly adjustments and day-of-week 

adjustments. Emissions are known to be quite different for a typical weekday versus a typical 

Saturday or Sunday. For the day-of-week temporal processing step, emissions may be processed 

using representative Monday, weekday, Saturday, and Sunday for each month; we refer to this 

type of processing here as MWSS processing (note that because SMOKE operates in Greenwich 

Mean Time [GMT] then Monday would include some of local time Sunday so needs to be 

processed separately from the typical weekday). This approach significantly reduces the number 

of times the temporal processing step must be run. In the sections below, we have identified the 

cases in which we have used the MWSS processing approach. Figure 2-2 provides a schematic 

diagram of SMOKE/BEIS2 processing steps used in this project to generate biogenic emissions 

rates for Volatile Organic Compounds (VOCs) and oxides of nitrogen (NOx). Because biogenic 

emissions are temperature sensitive, they are generated for each day of 2002 using day-specific 

meteorological conditions from the MM5 meteorological model. 
 

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Figure 2-2. Flow diagram of SMOKE/BEIS2 processing steps.

2.1.2 SMOKE Scripts 

The scripts are the interface that emissions modelers use to run SMOKE and define the set up 

and databases used in the emissions modeling so are important for anyone wishing to reproduce 

the CENRAP SMOKE emissions modeling. Many iterations of the CENRAP SMOKE 

emissions modeling were performed using updated and corrected emissions data and 

assumptions resulting in the creation of numerous SMOKE modeling scripts during the course of 

the study. For the CENRAP annual 2002 SMOKE emissions modeling, the default SMOKE 

script set up, which is based on source categories, was used to configure the scripts. We made 

several modifications to the default SMOKE scripts to modularize them, add error checking 

loops, and break up the report and logs directories by source category. The result is one script for 

each major source category being modeled that calls all of the SMOKE programs required for 

simulating that source category. 16 major source categories were modeled by SMOKE for 

CENRAP. An addition seven SMOKE scripts were also run to set up the emissions modeling. 

Table 2-1 lists all of the SMOKE scripts used for the 2002 base year modeling and the SMOKE 

programs called by each script. In addition to the source-specific scripts listed in Table 2-1, we 

also listed the SMOKE utility scripts that actually call executables, manage the log files, and 

manage the configuration of the SMOKE simulations.
 

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Table 2-1. Summary of SMOKE scripts. 

Source Category Script Name 

SMOKE 

Programs/Functions 

Area /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_ar_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

Area fire /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_arf_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

Offshore Area /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_ofsar_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

Non-road* 

Mobile 

/home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_nr_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

Fugitive dust /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_fd_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

Road dust /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_rd_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

Ammonia* /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_nh3_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

On-road 

Mobile (non-VMTbased) 

/home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_mb_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

On-road non-US 

Mobile (non-VMTbased) 

/home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_nusm_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

On-road Mobile 

(VMT-based) 

/home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_mbv_base02f.csh 

smkinev, mbsetup, grdmat, 

spcmat, premobl, emisfac, 

temporal, smkmerge, smkreport 

WRAP Oil and Gas /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_wog_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

Point /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_pt_base02f.csh 

smkinev, grdmat, spcmat, laypoint, 

temporal, smkmerge, smkreport 

Offshore point /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_ofs_base02f.csh 

smkinev, grdmat, spcmat, laypoint, 

temporal, smkmerge, smkreport 

Canadian Point fires /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_bsf_base02f.csh 

smkinev, grdmat, spcmat, laypoint, 

temporal, smkmerge, smkreport 

All point fires /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_alf_base02f.csh 

smkinev, grdmat, spcmat, laypoint, 

temporal, smkmerge, smkreport 

Biogenec /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_bg_base02f.csh 

Normbies3, tmpbies3, smkmerge 

n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/make_invdir.csh 

builds output file names and 

directories 

n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/smk_run.csh 

Calls SMOKE executables for 

everything but projection, controls, 

and QA 

n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/qa_run.csh 

Calls the SMOKE executables for 

running QA program & names the 

input/output directories for reports 

n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smoke_calls.csh 

Calls smk_run.csh, qa_run.csh, 

configuration and management 

n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

Assignes/ASSIGNES.cenrap_base02f.cmaq.cb4

p25 

Sets up the environment variables 

for use of SMOKE 

n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

Assignes/smk_mkdir 

Creates the input/output 

directories 

n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

Assignes/setmerge_files.scr 

Sets up the output environment 

variables for the smkmerge 

program * The nr and nh3 where farther divided to nrm and nry and nh3m and nh3y for the monthly/seasonal and yearly inventories
 

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2.1.3 SMOKE Directory Structures 

The SMOKE directories can be divided into three broad categories: 

1. Program Directories: These directories contain the model source code, assigns files, 

scripts and executables needed to run SMOKE. 

2. Input Directors: These directories contain the raw emissions inventories, the 

meteorological data and the ancillary input files. 

3. Output Directories: These directories contain all of the output from the model. Also, the 

output directories contain the MOBILE6 input files. 

The directories are described in the Table 2-2. The final pre-merged emission file names and 

sources of the data re provided in Appendix B. 

Table 2-2. Summary of SMOKE directories. 

Category Directory Location Directory Contents 

/home/aqm2/edss2/ cenrap02f/subsys/smoke/src SMOKE source code 

/home/aqm2/edss2/ 

cenrap02f/subsys/smoke/assigns 

SMOKE assigns files 

/home/aqm2/edss2/ cenrap02f/subsys/smoke/scripts SMOKE make and run 

scripts 

Program 

/home/aqm2/edss2/ 

cenrap02f/subsys/smoke/Linux2_x86pg 

SMOKE executables 

/home/aqm2/edss2/ cenrap02f/data/met MCIP out metrology files 

/home/aqm2/edss2/ cenrap02f/data/ge_dat SMOKE ancillary input files 

Input 

/home/aqm2/edss2/ 

cenrap02f/data/inventory/cenrap2002 

Raw emissions inventory 

files 

/home/aqm2/edss2/ 

cenrap02f/data/run_base02f/static 

Non-time dependent SMOKE 

intermediate outputs and 

MOBILE6 inputs 

/home/aqm2/edss2/ cenrap02f/ 

data/run_base02f/scenario 

Time dependent SMOKE 

intermediate outputs 

/home/aqm2/edss2/ 

cenrap02f/data/run_base02f/outputs 

Model-ready SMOKE 

outputs 

Output 

/home/aqm2/edss2/ cenrap02f/data/reports SMOKE QA reports 

2.1.4 SMOKE Configuration 

SMOKE was configured to generate emissions for all months of 2002 on the 36-km unified RPO 

modeling domain (Figure 1-2). For the anthropogenic emissions sources that use hourly 

meteorology and daily or hourly data (i.e., on-road mobile sources, point sources with CEM data, 

point source fires and biogenic sources) we configured SMOKE to represent the daily emissions 

explicitly. For the non-meteorology dependent emissions, we used a representative Saturday, 

Sunday, Monday, and weekday for each month as surrogate days for the entire month’s 

emissions (we refer to this as the MWSS processing approach). For these non-meteorology 

dependent emissions sources we explicitly represented the holidays as Sundays. Table 2-3 lists 

the days that we modeled as representative days in the months that we simulated for the 2002 

base year modeling. Table 2-4 lists the holidays in 2002 that were modeled as Sundays.
 

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We used the designations in Table 2-5 to determine which months fell into each season when 

temporally allocating the seasonal emissions inventories. Some of the inventories for the 

Electrical Generating Units (EGUs) were received for Winter and Summer. Table 2-6 

determines which months fell into each season 
 

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Table 2-5. Assignments of months to four seasons for use of 

seasonal inventory files in SMOKE.

Table 2-6. Assignments of months to two seasons for use of 

seasonal inventory files in SMOKE. 

2.1.5 SMOKE Processing Categories 

Emissions inventories are typically divided into area, on-road mobile, non-road mobile, point, 

and biogenic source categories. These divisions arise from differing methods for preparing the 

inventories, different characteristics and attributes of the categories, and how the emissions are 

processed through models. Generally, emissions inventories are divided into the following 

source categories, which we refer to later as “SMOKE processing categories.” 

• Stationary Area Sources: Sources that are treated as being spread over a spatial extent 

(usually a county or air district) and that are not movable (as compared to non-road 

mobile and on-road mobile sources). Because it is not possible to collect the emissions at 

each point of emission, they are estimated over larger regions. Examples of stationary 

Month Season 

January Winter 

February Winter 

March Winter 

April Winter 

May Summer 

June Summer 

July Summer 

August Summer 

September Summer 

October Winter 

November Winter 

December Winter 
 

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area sources are residential heating and architectural coatings. Numerous sources, such as 

dry cleaning facilities, may be treated either as stationary area sources or as point sources. 

• On-Road Mobile Sources: Vehicular sources that travel on roadways. These sources can 

be computed either as being spread over a spatial extent or as being assigned to a line 

location (called a link). Data in on-road inventories can be either emissions or activity 

data. Activity data consist of vehicle miles traveled (VMT) and, optionally, vehicle 

speed. Activity data are used when SMOKE will be computing emission factors via 

another model, such as MOBILE6 (U.S. EPA, 2005). Examples of on-road mobile 

sources include light-duty gasoline vehicles and heavy-duty diesel vehicles. 

• Non-Road Mobile Sources: These sources are engines that do not always travel on 

roadways. They encompass a wide variety of source types from lawn and garden 

equipment to locomotives and airplanes. Emission estimates for most non-road sources 

come from EPA’s NONROAD model (OFFROAD in California). The exceptions are 

emissions for locomotives, airplanes, pleasure craft and commercial marine vessels. 

• Point Sources: These are sources that are identified by point locations, typically because 

they are regulated and their locations are available in regulatory reports. In addition, 

elevated point sources will have their emissions allocated vertically through the model 

layers, as opposed to being emitted into only the first model layer. Point sources are often 

further subdivided into electric generating unit (EGU) sources and non-EGU sources, 

particularly in criteria inventories in which EGUs are a primary source of NOx and SO2. 

Examples of non-EGU point sources include chemical manufacturers and furniture 

refinishers. Point sources are included in both criteria and toxics inventories. 

• Biogenic Land Use Data: Biogenic land use data characterize the types of vegetation that 

exist in either county-total or grid cell values. The biogenic land use data in North 

America are available using two different sets of land use categories: the Biogenic 

Emissions Landcover Database (BELD) version 2 (BELD2), and the BELD version 3 

(BELD3) (CEP, 2004b). 

In addition to these standard SMOKE processing categories, we have added other categories 

either to represent specific emissions processes more accurately or to integrate emissions data 

that are not compatible with SMOKE. Examples of emissions sectors that fall outside of the 

SMOKE processing categories include emissions generated from process-based models for 

representing windblown dust and agricultural ammonia (NH3) sources. An emissions category 

with data that are not compatible with SMOKE is one with gridded emissions data sets, such as 

commercial marine sources. Another nonstandard emissions category that we modeled was 

emissions from fires. All of the emissions categories that we used to build CENRAP simulations 

are described in detail in the following sections. 

Continuing the enhancement of the emissions source categories that we initiated during the 

preliminary 2002 modeling, we further refined the categories from the standard definitions listed 

above to include more explicit emissions sectors. The advantage of using more detailed 

definitions of the source categories is that it leads to more flexibility in designing control 

strategies, substituting new inventory or profile data into the modeling, managing the input and 

output data from SMOKE and conducting QA of the SMOKE outputs. The major drawback to 

defining more emissions source categories is the increased level of complexity and 

computational requirements (run times and disk space) that results from having a larger number 

of input data sets. Another motivation behind separating the various emissions categories is 

related to the size and flexibility of the input data. Some data sets, like the CENRAP on-road 
 

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mobile inventory, were so large that we had to process them separately from the rest of the 

sources in the on-road sector due to computational constraints. We also separated the non-road 

mobile and ammonia sectors into yearly and monthly inventories to facilitate the application of 

uniform monthly temporal profiles to the monthly data. Additional details about how we 

prepared the emissions inventories and ancillary data for modeling are described in Sections 2.2 

through 2.16. Table 2-7 summarizes the entire group of source sectors that composed simulation 

Typ02G. Each emissions sector listed in the table represents an explicit SMOKE simulation. As 

discussed in Section 2.1.2 below, after finishing all of the source-specific simulations, we used 

SMOKE to combine all of the data into a single file for each day for input to the air quality 

modeling systems. Each subsection on the emissions sectors describes each sector in terms of the 

SMOKE processing category, the year covered by the inventory, and the source(s) of the data. 

Additional details about the inventories are also provided, including any modifications that we 

made to prepare them for input into SMOKE. 

Table 2-7. CENRAP Typ02G emissions categories. 

Emissions Sector Abbreviation* 

Fires as Point Sources (WRAP, CENRAP, 

VISTAS) 

Alf 

Area Sources (All domain) ar 

CENRAP area fires arf 

Area fires, Anthropogenic (All domain, excluding 

WRAP and CENRAP) 

arfa 

Area fires, Wild (All domain, excluding WRAP) arfw 

Biogenic b3 

Ontario, Canada, point-source fires bsf 

Fugitive dust fd 

WRAP on-road mobile mb 

CENRAP on-road mobile mbv_CENRAP 

Other US on-road mobile mbv 

Monthly CENRAP/MRPO anthropogenic NH3 nh3m 

Ammonia from annual inventory (CENRAP) nh3y 

WRAP anthropogenic NH3 nh3 

Seasonal/Monthly non-road mobile (WRAP, 

CENRAP, MW) 

nrm 

Annual non-road mobile nry 

On-road Mobile (Non-US) nusm 

Offshore shipping (Gulf, Atlantic) ofs 

Offshore area (Gulf) ofsar 

Stationary point (All domain, including offshore) pt 

Road dust rd 

Windblown dust (All domain) wb_dust 

WRAP oil and gas wog 

*These abbreviations are used in the file naming of the SMOKE output files for each sector. 
 

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Emissions models such as SMOKE are computer programs that convert annual or daily 

estimates of emissions at the state or county level to hourly emissions fluxes on a uniform spatial 

grid that are formatted for input to an air quality model. For the Typ02G and Base18G emission 

inventories we prepared emissions for CMAQ version 4.5 using SMOKE version 2.1 on the 

UCR Linux computing cluster. SMOKE integrates annual county-level emissions inventories 

with source-based temporal, spatial, and chemical allocation profiles to create hourly emissions 

fluxes on a predefined model grid. For elevated sources that require allocation of the emissions 

to the vertical model layers, SMOKE integrates meteorology data to derive dynamic vertical 

profiles. In addition to its capacity to represent the standard emissions processing categories, 

SMOKE is also instrumented with the Biogenic Emissions Inventory System, version 3 (BEIS3) 

model for estimating biogenic emissions fluxes (U.S. EPA, 2004) and the MOBILE6 model for 

estimating on-road mobile emissions fluxes from county-level vehicle activity data (U.S. EPA, 

2005a). 

SMOKE uses C-Shell scripts as user interfaces to set configuration options and call executables. 

SMOKE is designed with flexible QA capabilities to generate standard and custom reports for 

checking the emissions modeling process. After modeling all of the source categories individually, including those categories generated outside of SMOKE, we used SMOKE to merge all of 

the categories together to create a single CMAQ input file per simulation day. Also, for use in 

the CAMx modeling, we converted the CMAQ-ready emissions estimates to CAMx-ready files 

using the CMAQ2CAMx converter. Additional technical details about the version of SMOKE 

used for final simulations are available from CEP (2004b). All scripts, data, and executables used 

to generate the Typ02G and Base18G emissions for CMAQ and CAMx are archived on the 

CENRAP computing cluster. 

2.1.6 2002 and 2018 Data Sources 

This section describes the procedures that the CENRAP followed to collect and prepare all 

emissions data for Typ02G and Base18G simulations. We discuss the sources of all inventory 

and ancillary data used for simulations. CENRAP worked with emissions inventory contractors, 

other RPOs, and EPA to collect all of the data that constitute the simulation. Table 2-8 lists all of 

the contacts for the various U.S. anthropogenic emission inventories we used. For the CENRAP 

inventories, this table lists the contacts for the contractors who prepared the inventories; for the 

non-CENRAP inventories it lists the contacts at the RPOs who provided us inventory data. We 

obtained the emissions inventories for Canada and Mexico from the U.S. EPA Emissions Factors 

and Inventory Group (EFIG) via the Clearinghouse for Inventories and Emissions Factors 

(CHIEF) website (http://www.epa.gov/ttn/chief/index.html). 
 

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Table 2-8. CENRAP anthropogenic emissions inventory contacts. 

Source Category Emissions Data Contact 

WRAP 

All Tom Moore, Western Governors' Association 

Phone: (970) 491-8837 

Email: mooret@cira.colostate.edu

CENRAP 

2002 Consolidated Inventory Randy Strait, E.H. Pechan & Assoc., Inc. 

Phone: 919-493-3144 

Email: rstrait@pechan.com 

NH3 Inventory, Prescribed and 

Agricultural Fires, and On-road mobile 

emissions 

Dana Sullivan, Sonoma Technology, Inc. 

Phone: 707-665-9900 

Email: dana@sonomatech.com

Gulf Off-shore platform and support 

vessel emissions 

Holly Ensz, Minerals Management Service 

Phone: (504) 736-2536 

Email: holli.ensz@mms.gov

VISTAS 

All Greg Stella, Alpine Geophysics, LLC, 

Phone: 828-675-9045 

Email: gms@alpinegeophysics.com 

MANE-VU 

All Megan Schuster, MARAMA, 

Baltimore, MD USA 

Phone: 410-467-0170 

Email: mschuster@marama.org 

MRPO 

All Mark Janssen, LADCO, 

Des Plaines, IL, USA 

Phone: 847-296-2181 

Email:janssen@ladco.org 

As mentioned above, the refinement of these inventories involved splitting some of the inventory 

files into more specific source sectors. As the stationary-area-source emissions sector has 

traditionally been a catch-all for many types of sources, this is the inventory sector that required 

the greatest amount of preparation. Upon receiving all stationary-area-source inventories we 

extracted fugitive dust, road dust, anthropogenic NH3, and for the non-WRAP U.S. inventories, 

stage II refueling sources. We retained the dust sources as separate categories that we would 

further refine with the application of transport factors (see Section 2.8). 

We collected the ancillary data used for SMOKE modeling from several sources. SMOKE 

ancillary modeling data include: 

• Temporal and chemical allocation factors by state, county, and source classification code 

(SCC); 

• Spatial surrogates and cross-reference files for allocating county-level emissions to the 

model grid; 

• Hourly gridded meteorology data; 

• Stack defaults for elevated point sources; 

• MOBILE6 configuration files; 

• A Federal Implementation Standards (FIPS) codes (i.e., country/state/county codes) 

definition file; 
 

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• A Source Category Classification (SCC) codes definition file; 

• A pollutant definition file; and 

• Biogenic emission factors. 

Except for the meteorology data and the MOBILE6 configuration files, we used default data sets 

provided by EPA as the basis for all of the ancillary data except for temporal profiles used for 

Electric Generating Units (EGUs). These profiles were developed based on CEM data from 2000 

through 2003 (Pechan and CEP, 2005c). CENRAP provided the meteorology data for the 

simulations at 36-km and 12-km grid resolutions (Johnson, 2007). The inventory contractor who 

prepared the MOBILE6 inventories provided the MOBILE6 configuration files either directly or 

via an RPO representative; details about the sources of the MOBILE6 inputs are provided in 

Section 2.4. We made minor modifications to the chemical allocation, pollutant definition, and 

country/state/county codes files for new sources, pollutants, or counties contained in the 

inventories that we had not previously modeled. We made major modifications to the temporal 

and spatial allocation inputs, as described below. 

2.1.7 Temporal Allocation 

Temporally allocating annual, daily, or hourly emissions inventories in SMOKE involves 

combining a temporal cross-reference file and a temporal profiles file. 

• Temporal cross-reference files associate monthly, weekly, and diurnal temporal profile 

codes with specific inventory sources, through a combination of a FIPS 

(country/state/county) code, an SCC, and sometimes for point sources, facility and unit 

identification codes. 

• Temporal profiles files contain coded monthly, weekly, and diurnal profiles in terms of a 

percentage of emissions allocated to each temporal unit (e.g., percentage of emissions per 

month, weekday, or hour). 

As a starting point for the temporal allocation data for simulations, we used the files generated 

by emission inventory contractors (Pechan and CEP, 2005c). Based on guidance from the 

developers of some of the inventory files, we enhanced the temporal profiles and assignments for 

some source categories (Pechan, 2005b). 

We modified the temporal allocation data for the simulations to improve the representation of 

temporal emissions patterns for certain source categories. We implemented the adjusted profiles 

in SMOKE by modifying the temporal cross-reference file for the applicable FIPS and SCC 

combinations. 

Updated temporal profiles for EGUs were made available for MRPO in the MRPO Base K 

inventory. Since the non-road emissions for IA and MN were monthly emissions developed by 

MRPO, new temporal profiles were created for all the SCCs in these emissions files for these 

two states only. The monthly profile was uniform and the weekly and diurnal profiles were kept 

the same as were modeled for the rest of the country. 

An updated temporal profile, profile 485, based on NOAA 1971-2000 population weighted 

average heating degree days for home heating area source emissions was obtained from 
 

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VISTAS. This profile provided state specific updates for home heating emissions and was 

applied to the full inventory in place of profile 17XX. 

Other additions to the Base02G temporal allocation data included updates that made by other 

RPOs that are applicable to their inventories. These other updates to the temporal allocation files 

included 

• VISTAS continuous emissions monitoring (CEM)-specific profiles for EGUs in the 

VISTAS states; 

• VISTAS agricultural burning profiles; 

• Wildfire and prescribed fire profiles developed by VISTAS for the entire U.S.; 

• MANE-VU on-road mobile profiles; 

• WRAP weekly and diurnal road dust profiles; 

• WRAP diurnal wildfire, agricultural fire, and prescribed fire profiles; and 

• WRAP on-road mobile weekly and diurnal profiles. 

Finally, for all of the monthly and seasonal emissions inventories, we modified the temporal 

cross-reference files to apply uniform monthly profiles to the sources contained in these 

inventories. The monthly variability is inherent in monthly and seasonal inventories and does not 

need to be reapplied through the temporal allocation process in SMOKE. The inventories to 

which we applied uniform monthly temporal profiles included: 

• WRAP, CENRAP, and MRPO non-road mobile sources; 

• WRAP on-road mobile sources; 

• WRAP road dust; and 

• CENRAP anthropogenic ammonia. 

2.1.8 Spatial Allocation 

SMOKE uses spatial surrogates and SCC cross-reference files to allocate county-level emissions 

inventories to model grid cells. Geographic information system (GIS)-calculated fractional land 

use values define the percentage of a grid cell that is covered by standard sets of land use 

categories. For example, spatial surrogates can define a grid cell as being 50% urban, 10% forest, 

and 40% agricultural. In addition to land use categories, spatial surrogates can also be defined by 

demographic or industrial units, such as population or commercial area. Similar to the temporal 

allocation data, an accompanying spatial cross-reference file associates the spatial surrogates 

(indexed with a numeric code) to SCCs. Spatial allocation with surrogates is applicable only to 

area and mobile sources that are provided on a county level basis. Point sources are located in the 

model grid cells by SMOKE based on the latitude-longitude coordinates of each source. 

Biogenic emissions are estimated based on 1-km2

 gridded land use information that is mapped to 

the model grid using a processing program such as the Multimedia Integrated Modeling System 

(MIMS) Spatial Allocator (CEP, 2004). 

We used various sources of spatial surrogate information for the U.S., Canada, and Mexico 

inventories in the simulations. For the U.S. and Canadian sources, we used the EPA unified 
 

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surrogates available through the EFIG web site (EPA, 2005c). For the 36-km grid, EPA provides 

these data already formatted for SMOKE on the RPO Unified 36-km domain that we used for the 

simulations. We modified the spatial surrogates for Canada on the RPO Unified 36-km domain 

by adopting several surrogate categories that were enhanced by the WRAP. Table 2-9 provides 

details about the new Canadian spatial surrogates that were developed by the WRAP and used 

for CENRAP simulations. For modeling Mexico, we used Shapefiles developed for the Big Bend 

Regional Aerosol and Visibility Observations Study (BRAVO) modeling to create surrogates for 

Mexico on the RPO Unified 36-km domain (EPA, 2005c). 

 Table 2-9. New Canadian spatial surrogates.

Attribute Base02a Code Shapefile Reference 

Land area 950 can_land93_land Natural Resources Canada (1993) 

AVHRR land cover data 

Water area 951 can_land93_water Natural Resources Canada (1993) 

AVHRR land cover data 

Forest land area 952 can_land93_forest Natural Resources Canada (1993) 

AVHRR land cover data 

Agricultural land area 953 can_land93_agri Natural Resources Canada (1993) 

AVHRR land cover data 

Urban land area 954 can_land93_urban Natural Resources Canada (1993) 

AVHRR land cover data 

Rural land area 955 can_land93_rural Natural Resources Canada (1993) 

AVHRR land cover data 

Airports 956 can_airport U.S. DOT Bureau of Transportation Statistics (2005) NORTAD 

1:1,000,000 scale data 

Ports 957 can_port U.S. DOT Bureau of Transportation Statistics (2005) NORTAD 

1:1,000,000 scale data 

Roads 958 can_road1m Natural Resources Canada (2001) 

National Scale Frameworks data 

Rail 959 can_rail1m Natural Resources Canada (1999) 

National Scale Frameworks data 

2.2 Stationary Point Source Emissions 

Stationary-point-source emissions data for SMOKE consist of (1) Inventory Data Analyzer 

(IDA)-formatted inventory files; (2) ancillary data for allocating the inventories in space, time, 

and to the Carbon Bond-IV chemistry mechanism used in CMAQ and CAMx; and 

(3) meteorology data for calculating plume rise from the elevated point sources. This section 

describes where CENRAP obtained these data, how we modeled them, and the types of QA that 

we performed to ensure that SMOKE processed the data as expected. 
 

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2.2.1 Data Sources 

For the stationary-point-source inventories in Typ02G and Base18G, we used actual 2002 data 

developed by the RPOs for the U.S., version 2 of the year 2000 Canadian inventory, and the 

BRAVO 1999 Mexican inventory. The BRAVO inventory was updated with entirely new 

inventories for the six northern states of Mexico for stationary area, as well as stationary point, 

on-road mobile, and off-road mobile sources. Emissions for the southern states of Mexico were 

included for the first time in CENRAP simulations Typ02G and Base18G. These data were 

provided by ERG, Inc., who completed an updated 1999 emissions inventory for northern 

Mexico (ERG, 2006b) and delivered these data to the WRAP. The CENRAP stationary-point 

inventory consisted of annual county-level and tribal data provided in August of 2005 (Pechan 

and CEP, 2005e). The WRAP (ERG, 2006a) and VISTAS Base G (MACTEC, 2006) stationarypoint inventories consisted of an annual data set and monthly CEM data for selected EGUs. The 

WRAP and VISTAS provided these data directly to CENRAP. We downloaded the MANE-VU 

stationary-point inventories from the MANE-VU web sites. MRPO base K data was 

downloaded and processed for SMOKE modeling by Alpine Geophysics under contract from 

MARAMA. UCR entered into a nondisclosure agreement with Environment Canada to obtain 

version 2 of the 2000 Canadian point-source inventory. This inventory represented a major 

improvement over the version of the data that we had used in the preliminary 2002 modeling. 

Reductions anticipated from BART controls for electric generating units (EGU) in Oklahoma, 

Arkansas, Kansas, and Nebraska were included in projections of 2018 emissions. These 

anticipated reductions were based on actual operating conditions and estimated control 

efficiencies from utilities. 

Newly permitted coal-fired utilities were included in 2018 projections. Conservatively, no IPM 

projected new units were removed from the simulation with the addition of the permitted 

facilities. 

Due to missing or clearly erroneous stack parameters, several facilities in CENRAP states were 

relegated to default stack profiles based on SCC in the NEI QA process. Prioritizing for the 

largest emissions sources, these default parameters were corrected by CENRAP States and 

updated files were provided to modeling contractors. Final IDA input files Typ02G and 

Base18G for point sources reflect State corrections. 

For coal-fired point and area sources, The EPA Office of Air Quality and Planning Standards 

(OAQPS) determined that the organic carbon fraction in the speciation profile code "NCOAL" 

was not representative of most coal combustion occurring in the U.S. This profile has an organic 

carbon fraction of 20%, which includes an adjustment factor of 1.2 to account for other atoms 

(like oxygen) attached to the carbon. OAQPS has reverted back to the profile code "22001" for 

coal combustion, which has an organic carbon fraction of 1.07% (again including the 1.2 factor 

adjustment). This is the same profile that EPA used for previous rulemaking efforts including 

the Heavy Duty Diesel Rule and Non-Road Rule, which were proposed (and publicly reviewed) 

prior to the introduction of the NCOAL profile. 

The consensus in OAQPS is that the NCOAL profile has a high organic carbon percentage 

because it is based on measurements of combustion of lignite coal. With the exception of Texas, 

lignite is not widely used in the U.S.. Thus, OAQPS staff stopped relying on this profile as a 

national default profile. A new coal speciation profile developed based on Eastern bituminous 
 

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coal combustion (since much of the coal burned in the U.S. is of this type) is being developed by 

EPA's Office of Research and Development but was not completed for this study. 

The profile recently developed for MRPO by Carnegie Mellon was provided to CENRAP and is 

representative of combustion of eastern bituminous coal. This profile is a more appropriate 

profile for most facilities in the U.S. than the default NCOAL profile. 

Additionally, the "22001" profile has been flagged as problematic because of the apparent 

inadvertent switching of the organic carbon and elemental carbon fractions, which are 1.07% and 

1.83% respectively. The report discovering the discrepancy in the profile did not offer a clear 

alternative to correct the problem (MACTEC, 2003). 

CENRAP has continued to use the NCOAL factor for facilities burning lignite in North Dakota 

and Texas. For the remainder of the U.S., the MRPO profile, CMU, was used. The NCOAL 

factor was modified reducing the organic carbon by half and assigning the remainder to PM2.5. 

The modification was at the request of Texas and was reflective of the original study for the 

NCOAL factor conducted in Texas (Chow, 2005). Table 2-10 summarizes the PM2.5 speciation 

profiles for the NCOAL, 2201 and CMU speciation profiles for coal burning sources. 

Table 2-10. PM 2.5 speciation profiles for coal-burning sources.

Profile POC PEC PNO3 PSO4 PM2.5 

NCOAL 0.1000 0.0100 0.0050 0.1600 0.7250 

22001 0.0107 0.0183 0.0000 0.1190 0.8520 

CMU 0.0263 0.0315 0.0036 0.0447 0.8938 

Final simulations used improved temporal allocation and speciation information relative to the 

preliminary 2002 modeling; the rest of the ancillary data for modeling stationary point sources 

stayed the same (Mansell et al., 2005). 

2.2.2 Emissions Processing 

For Typ02G and Base18G simulations we configured SMOKE to process the annual inventories 

for the U.S., Canada, and Mexico and process hourly CEM data for the VISTAS. We configured 

SMOKE to allocate these emissions up to model layer 15 (approximately 2,500 m AGL), which 

roughly corresponds to the maximum planetary boundary layer (PBL) heights across the entire 

domain throughout the year. As coarse particulate matter (PMC) is not an inventory pollutant but 

is required by the air quality models as input species, we used SMOKE to calculate PMC during 

the processing as (PM10 - PM2.5). With the SMOKE option WKDAY_NORMALIZE set to “No,” 

we treated the annual inventories based on the assumption that they represent average-day data 

based on a seven-day week, rather than average weekday data. We also assumed that all of the 

volatile organic compound (VOC) emissions in the inventories are reactive organic gas (ROG), 

and thus used SMOKE to convert the VOC to total organic gas (TOG) before converting the 

emissions into CB-IV speciation for the air quality models. To capture the differences in diurnal 

patterns that are contained in the CEM temporal profiles for VISTAS and CENRAP states 

(Base02F), we configured SMOKE to generate daily temporal matrices, as opposed to using a 

Monday-weekday-Saturday-Sunday (MWSS) temporal allocation approach. 
 

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To QA the stationary-point emissions, we used the procedures in the CENRAP emissions 

modeling QA protocol (Morris and Tonnesen, 2004) and a suite of graphical summaries. We 

used tabulated summaries of the input data and SMOKE script settings to document the data and 

configuration of SMOKE for all simulations. These QA graphics are available on the web site 

at: http://pah.cert.ucr.edu/aqm/cenrap/emissions.shtml 

2.2.3 Uncertainties and Recommendations 

There were issues with the stationary-point emissions that we left unresolved at the completion 

of the Typ02G and Base18G emissions modeling either because we did not feel they would have 

a major impact on the modeling results in CENRAP states or because we did not have alternative 

approaches and they represented the best available information. Canadian emissions for 2000 

were found to have a significant number of missing stack parameters. These stacks when 

modeled with default parameters frequently resulted in lower plume heights. Stack parameters 

for 2000 were corrected based on cross referencing sources with the 2005 Canadian inventory for 

the largest emitting points. Stack parameters for many of the sources with lower emissions 

remain incorrect, but are assumed to have a less significant impact on CENRAP Class I areas. 

The 2020 projected emissions for Canada were obtained as air quality model-ready files from 

EPA. EPA has not confirmed that missing stack parameters were corrected for the projected 

inventory. It is assumed that they were not corrected and default parameters were used instead. 

Given confidentiality issues that surround Canadian inventories, EPA processed emissions 

represent the best available data. 

2.3 Stationary Area Sources 

Stationary-area-source emissions data for SMOKE consist of IDA-formatted inventory files and 

ancillary data for allocating the inventories in space, time, and to the Carbon Bond-IV chemistry 

mechanism used in CMAQ and CAMx. This section describes where we obtained these data, 

how we modeled them, and the types of QA that we performed to ensure that SMOKE processed 

the data as expected. 

2.3.1 Data Sources 

For the stationary area source inventories in the Typ02G and Base18G simulations, we used 

actual 2002 data developed by the RPOs for the U.S., version 2 of the year 2000 Canadian 

inventory, and the updated Mexican inventory, http://www.epa.gov/ttn/chief/net/mexico.html. 

The BRAVO inventory was updated with entirely new inventories for the six northern states of 

Mexico for stationary area, as well as stationary point, on-road mobile, and off-road mobile 

sources. Emissions for the southern states of Mexico were included for the first time in 

CENRAP simulations Typ02G and Base18G. The CENRAP stationary-area inventory consisted 

of annual county-level and tribal data provided by in August of 2005 (Pechan and CEP, 2005e). 

The WRAP (ERG, 2006a) and VISTAS Base G (MACTEC, 2006) stationary-area inventories 

consisted of an annual data set. We downloaded the MANE-VU stationary-area inventories from 

the MANE-VU web sites. MRPO base K data was downloaded and processed for SMOKE 

modeling by Alpine Geophysics under contract from MARAMA. 
 

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To prepare the stationary-area inventories for modeling, we made several modifications to the 

files by removing selected sources either to model them as separate source categories or to omit 

them from simulations completely. Using guidance provided by EPA (EPA, 2004b), we 

extracted fugitive and road dust sources from all stationary-area inventories for adjustment by 

transport factors and modeling as separate source categories (see Section 2.8). We also extracted 

and discarded the stage II refueling sources (Table 2-11) from the U.S. inventories; we modeled 

these sources with MOBILE6 as part of the on-road mobile-source emissions. We left the stage 

II refueling emissions in the WRAP stationary-area inventory because the on-road mobile 

inventory that we received for this region did not contain these emissions. 

Table 2-11. Refueling SCCs removed from the non-WRAP U.S. stationary-area inventory. 

SCC Description 

2501060100 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service 

Stations Stage 2: Total 

2501060101 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service 

Stations Stage 2: Displacement Loss/Uncontrolled 

2501060102 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service 

Stations Stage 2: Displacement Loss/Controlled 

2501060103 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service 

Stations Stage 2: Spillage 

2501070100 Storage and Transport Petroleum and Petroleum Product Storage Diesel Service 

Stations Stage 2: Total 

2501070101 Storage and Transport Petroleum and Petroleum Product Storage Diesel Service 

Stations Stage 2: Displacement Loss/Uncontrolled 

2501070102 Storage and Transport Petroleum and Petroleum Product Storage Diesel Service 

Stations Stage 2: Displacement Loss/Controlled 

2501070103 Storage and Transport Petroleum and Petroleum Product Storage Diesel Service 

Stations Stage 2: Spillage 

Other steps that we took to prepare the stationary-area inventories included confirming that there 

is no overlap between the anthropogenic NH3 inventory (Section 2.9) and stationary area 

sources, and moving area-source fires in each regional inventory to separate files. In addition to 

these inventory modifications we made a few changes to the ancillary data files for simulation 

Typ02G, as described next. 

Simulation Typ02G used improved temporal and spatial allocation information relative to the 

preliminary 2002 modeling; the rest of the ancillary data for modeling stationary area sources 

stayed the same as in the preliminary 2002 modeling (Mansell et al., 2005). We adopted 

enhanced spatial allocation data with additional area-based surrogates for Canada (Table 2-9), 

and added surrogates for a missing county in Colorado (Broomfield) from WRAP modeling and 

QA work. The WRAP had noticed when looking at the Canadian data for the preliminary 2002 

modeling that forest fire emissions from the Canadian area-source inventory, which are relatively 

large sources of CO, NOx, and PM2.5, were being allocated to a surrogate for logging activities. 

They found similar discrepancies for other area and non-road SCCs in Canada. To improve the 

representation of the Canadian emissions, we adopted several land-area-based surrogates 

developed by the WRAP, such as forested land area, urban land area, and rural land area, and 

made the accompanying additions to the spatial cross-reference file to associate inventory SCCs 

with these surrogates. We also added spatial surrogates for Broomfield County, CO; this county 

was included in the inventory but was not included in the base EPA surrogates (this county was 

recently created from portions of other counties). 
 

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Improvements to the temporal allocation data for simulation Typ02G included the addition of 

several FIPS-specific profiles provided by VISTAS and CENRAP contractors (Pechan 2005b). 

These temporal profiles listed in Table 2-12 targeted mainly fire and agricultural NH3 sources, 

such as open burning and livestock operations, respectively. 

Table 2-12. New Temporal Profile Assignments for CENRAP Area Source SCCs.

SCC Description Month Week Diurnal 

Recommend

ation Based 

on Profile 

Data for SCC 

Description of Similar 

SCC used to 

Recommend Profiles 

2310001000 Industrial Processes; Oil and 

Gas Production: SIC 13;All 

Processes : On-shore; Total: All 

Processes 

262 7 26 2310000000 Industrial Processes;Oil 

and Gas Production: SIC 

13;All Processes;Total: All 

Processes 

2310002000 Industrial Processes;Oil and 

Gas Production: SIC 13;All 

Processes : Off-shore;Total: All 

Processes 

262 7 26 2310000000 Industrial Processes;Oil 

and Gas Production: SIC 

13;All Processes;Total: All 

Processes 

2461870999 Solvent 

Utilization;Miscellaneous Nonindustrial: Commercial;Pesticide 

Application: NonAgricultural;Not Elsewhere 

Classified 

258 7 26 

2461800000 

Solvent 

Utilization;Miscellaneous 

Non-industrial: 

Commercial;Pesticide 

Application: All 

Processes;Total: All 

Solvent Types 

2805009200 Miscellaneous Area 

Sources;Agriculture Production 

- Livestock;Poultry production - 

broilers;Manure handling and 

storage 

1500 7 26 2805009300 Miscellaneous Area 

Sources;Agriculture 

Production - 

Livestock;Poultry 

production - broilers;Land 

application of manure 

2805021100 Miscellaneous Area 

Sources;Agriculture Production 

- Livestock;Dairy cattle - scrape 

dairy;Confinement 

1500 7 26 2805021300 Miscellaneous Area 

Sources;Agriculture 

Production - 

Livestock;Dairy cattle - 

scrape dairy;Land 

application of manure 

2805021200 Miscellaneous Area 

Sources;Agriculture Production 

- Livestock;Dairy cattle - scrape 

dairy;Manure handling and 

storage 

1500 7 26 2805021300 Miscellaneous Area 

Sources;Agriculture 

Production - 

Livestock;Dairy cattle - 

scrape dairy;Land 

application of manure 

2805023100 Miscellaneous Area 

Sources;Agriculture Production 

- Livestock;Dairy cattle - 

drylot/pasture 

dairy;Confinement 

1500 7 26 2805023300 Miscellaneous Area 

Sources;Agriculture 

Production - 

Livestock;Dairy cattle - 

drylot/pasture dairy;Land 

application of manure 

2805023200 Miscellaneous Area 

Sources;Agriculture Production 

- Livestock;Dairy cattle - 

drylot/pasture dairy;Manure 

handling and storage 

1500 7 26 2805023300 Miscellaneous Area 

Sources;Agriculture 

Production - 

Livestock;Dairy cattle - 

drylot/pasture dairy;Land 

application of manure 

2810020000 Miscellaneous Area 

Sources;Other 

Combustion;Prescribed Burning 

of Rangeland;Total 

3 11 13 2810015000 Miscellaneous Area 

Sources;Other 

Combustion;Prescribed 

Burning for Forest 

Management;Total 
 

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2.3.2 Emissions Processing 

For simulations Typ02G and Base18G we configured SMOKE to process the annual stationaryarea-source inventories for the U.S., Canada, and Mexico. As PMC is not an inventory pollutant 

but is required by the air quality models as input species, we used SMOKE to calculate PMC 

during the processing as (PM10 - PM2.5). With the SMOKE option WKDAY_NORMALIZE set 

to “Yes,” we treated the annual stationary-area inventories based on the assumption that they 

represent average weekday data, causing SMOKE to renormalize the data to a seven-day 

estimate before applying any temporal adjustments. We also assumed that all of the VOC 

emissions in the inventories are ROG and thus used SMOKE to convert the VOC to TOG before 

converting the emissions into CB-IV speciation for the air quality models. We configured 

SMOKE to use a MWSS temporal allocation approach, as opposed to a daily temporal approach. 

To QA the stationary-area emissions, we used the procedures in the CENRAP modeling QAPP 

and Modeling Protocol (Morris and Tonnesen, 2004; Morris et al., 2004a) and a suite of 

graphical summaries. We used tabulated summaries of the input data and SMOKE script settings 

to document the data and configuration of SMOKE for all simulations. The graphical QA 

summaries include, for all emissions output species, daily spatial plots summed across all model 

layers, daily time-series plots, and annual time-series plots. These QA graphics are available on 

the UCR/CENRAP web site at http://pah.cert.ucr.edu/aqm/cenrap/emissions.shtml . 

2.3.3 Uncertainties and Recommendations 

Most of the issues that we encountered with the stationary area sources related to the removal of 

certain SCCs from the base inventories for inclusion as other source categories or complete 

omission from simulations. We spent considerable effort on ensuring that we did not have 

overlap between the area inventory and the other sectors that explicitly represent sources 

traditionally contained in the area inventory, such as NH3 and dust. 

Both the Canadian and Mexican inventories presented minor problems that we resolved for 

simulation Typ02G but that can be addressed more thoroughly in future simulations. The 

Canadian inventory we used contained data only at the province level, essentially equivalent to a 

statewide rather than county-level inventory. A higher resolution inventory would have allowed 

us to use higher-resolution and more accurate spatial allocation data. Future modeling that uses 

Canadian data should move to the newly released municipality-level year 2000 inventories for 

Canada. 

There was a discrepancy between the state and county coding in the Mexican inventory and the 

SMOKE file that defines acceptable FIPS codes. Differences in the ordering of the Mexican state 

names between these two data sets led to some of the Mexican inventory sources being 

mislabeled in the SMOKE QA reports. The state codes in the inventory and spatial surrogate 

files for two Mexican states were changed to be consistent with the SMOKE 

country/state/county codes file. 
 

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2.4 On-Road Mobile Sources 

On-road mobile-source emissions data for SMOKE consist of IDA-formatted emissions and 

vehicle activity inventory files, and ancillary data for allocating the inventories in space, time, 

and to the Carbon Bond-IV chemistry mechanism used in CMAQ and CAMx. This section 

describes where we obtained these data, how we modeled them, and the types of QA that we 

performed to ensure that SMOKE processed the data as expected. 

2.4.1 Data Sources 

The SMOKE processing for CENRAP included two approaches for processing on-road mobile 

sources depending on the source of the data provided. The first approach was to compute mobile 

emissions values prior to providing them to SMOKE; we call this the pre-computed emissions 

approach. The second approach was to provide SMOKE with VMT data, meteorology data, and 

MOBILE6 inputs, and let the SMOKE/MOBILE6 module compute the mobile emissions based 

on these data; we call this the VMT approach. These approaches are not mutually exclusive for a 

single SMOKE run; therefore, we performed single SMOKE runs in which both approaches were 

used as follows: 

• Annual VMT for computing CO, NOx, VOC, SO2, NH3 and PM using MOBILE6 for all 

CENRAP States. 

• Pre-computed, seasonal MOBILE6-based emissions of all pollutants for the 13 WRAP 

states that included pre-speciated PM2.5 data. 

• Annual VMT for computing CO, NOx, VOC, SO2, NH3 and PM using MOBILE6 for the 

rest of the United States (VISTAS, MRPO and MANE-VU). 

• Pre-computed, annual 1999 emissions of all pollutants for Mexico. 

• Pre-computed, annual 2000 emissions of all pollutants for Canada. 

For the CENRAP states, STI provided VMT data and MOBILE6 input files for all counties in 

the CENRAP region (Reid et al., 2004a). MOBILE6 input files were provided only for the 

months of January and July for 2002. MOBILE6 input files for the remaining months of 2002 

had to be generated. These data were then processed within SMOKE. Using one set of 

MOBILE6 input files for each county in the CENRAP states resulted in compute memory 

requirements that were to large to process all CENRAP states together. Therefore the on-road 

mobile processing for the CENRAP states was split into two groups for SMOKE processing. The 

resulting gridded emissions data files were then merged together to obtain an on-road mobile 

source emissions file for the entire CENRAP region.

For the WRAP states we used actual 2002 data split into California and non-California seasonal 

inventories that were provided by the WRAP (Pollack et al., 2006). In addition to the standard 

criteria pollutants, these files contained pre-speciated PM2.5 emissions. For the rest of the U.S. 

we used annual county-level activity and speed inventories with monthly, county-level 

MOBILE6 inputs, and hourly meteorology to estimate the hourly emissions with the 

SMOKE/MOBILE6 module. For the non-U.S. inventories, we used version 2 of the year 2000 

Canadian inventory and the updated 1999 Mexican inventory pre-computed mobile source 

emissions. 
 

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2.4.2 Emissions Processing 

For the Typ02G emissions modeling we configured SMOKE to process the annual on-road 

mobile emissions inventory data for the WRAP, Canada, and Mexico as pre-computed 

inventories. For the non-WRAP states, we used the SMOKE/MOBILE6 integration to process 

the annual activity inventories and monthly, county-based roadway information. The WRAP 

inventories contained pre-computed speciated PM emissions (Pollack et al, 2006) so the SMOKE 

PM speciation module was not used. The WRAP on-road mobile inventories were developed to 

represent seven-day (weekly) average emissions (as compared to the area source inventory, 

which represented average weekday emissions). As actual weekly average emissions, we 

configured SMOKE to process the WRAP on-road mobile source emissions by setting 

WKDAY_NORMALIZE to “No” in which case the emissions are adjusted to represent weekday 

and Saturday and Sunday emissions (as in contrast to the area sources where the emissions are 

just adjusted for Saturday and Sunday). We also assumed that all of the VOC emissions in the 

inventories are ROG and used SMOKE to convert the VOC to TOG before converting the 

emissions into CB-IV speciation for the air quality models. We configured SMOKE to create 

day-of-week specific rather than MWSS, temporal profiles because the WRAP on-road mobile 

temporal profiles contain weekly profiles that vary across the weekdays. 

As noted previously, the large number of county roadway inputs for MOBILE6 processed for the 

non-WRAP portion of the U.S. required us to split the states mobile-source processing into three 

subsets because of computer memory limitations. Separate MOBILE6 input files were used for 

each separate county for CENRAP states, where as one MOBILE6 input file was used for several 

counties outside of the CENRAP region. The three subsets consisted of two sets of 

SMOKE/MOBILE6 simulations for the CENRAP and a simulation that computed on-road 

mobile emissions for the MRPO, VISTAS, and MANE-VU states. We configured MOBILE6 to 

use weekly temperature averaging for computing these emissions within SMOKE. 

To QA the on-road mobile emissions, we used the CENRAP emissions modeling QA protocol 

(Morris and Tonnesen, 2004; Morris et al., 2004a) and a suite of graphical summaries. We used 

tabulated summaries of the input data and SMOKE script settings to document the data and 

configuration of SMOKE for simulations Typ02G and Base18G. The graphical QA summaries 

include, for all emissions output species, daily spatial plots, daily time-series plots, and annual 

time-series plots. These graphics are available at 

http://pah.cert.ucr.edu/aqm/cenrap/qa_base02b36.shtml#mb

2.4.3 Uncertainties and Recommendations 

We approached the on-road mobile emissions preparation for simulation Typ02G from three 

different directions, which were based on the form of the input inventories and ancillary 

emissions data for different regions of the modeling domain: 

• The WRAP region used emissions estimates pre-computed with EMFAC for California 

and MOBILE6 for the rest of WRAP states and processed like area sources with SMOKE 

adjusted from weekly to day-of-week emissions. 

• The CENRAP, VISTAS, MRPO, and MANE-VU states used county-level activity data to 

compute emissions with the SMOKE/MOBILE6 module. 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_2_Emissions1.doc 2-23 

• The non-U.S. parts of the domain also had pre-computer on-road mobile source 

emissions so used an area-source approach for processing with SMOKE. 

Different approaches for modeling a single emissions sector adds complexity and additional 

sources of error and inconsistencies to the modeling because of the different assumptions that 

went into the preparation of the input data. For example, refueling emissions from the on-road 

mobile sector are represented in the WRAP area-source sector but are computed with MOBILE6 

for the rest of the U.S. Not using MOBILE6-based emissions for the non-U.S. portion of the 

domain neglects the effects of the actual 2002 meteorology on these emissions. Applying 

MOBILE6 outside of the U.S. is currently not possible because MOBILE6 is instrumented only 

for calculating emissions for the U.S. automotive fleet. The result of using MOBILE6 to 

calculate U.S. emissions and not using it to calculate the non-U.S. on-road mobile emissions 

estimates is that the non-U.S. emissions are not specific to this modeling year and the 2002 

meteorological conditions, whereas the U.S. emissions are 2002-specific. 

While we used the best available information to compute the on-road mobile emissions for the 

various portions of the modeling domain, inconsistent approaches for representing these 

emissions may lead to unnatural emissions gradients along political boundaries. We recommend 

for future work a unified approach for at least the U.S. inventories, where either we use 

MOBILE6 in SMOKE for the entire domain (or alternative emissions model such as 

CONCEPT), or we calculate the emissions with MOBILE6 outside of SMOKE and then use the 

resulting county-based emissions inventories. 

2.5 Non-Road Mobile Sources 

Non-road mobile source emissions data for SMOKE consist of annual, seasonal, and monthly 

IDA-formatted emission inventory files and ancillary data for allocating the inventories in space, 

time, and to the Carbon Bond-IV chemistry mechanism used in CMAQ and CAMx. This section 

describes where we obtained these data, how we modeled them, and the types of QA that we 

performed to ensure that SMOKE processed the data as expected. 

2.5.1 Data Sources 

The non-road mobile-source inventories in the Typ02G and Base18G emissions modeling used 

actual 2002 data developed by the RPOs for the U.S., version 2 of the year 2000 Canadian 

inventory and the improved 1999 Mexican inventory. The U.S. inventories consisted of annual, 

seasonal, and monthly inventories; the non-U.S. inventories were annual data. Pechan provided 

the CENRAP inventories divided between annual data for aircraft, locomotive, and commercial 

marine and annual files for all other non-road sources (Pechan and CEP, 2005e). Minnesota 

substituted the monthly MRPO Base K non-road inventory for the CENRAP inventory in their 

state. Iowa substituted the monthly estimates for non-road agricultural sources from the MRPO 

base K inventory for the CENRAP inventory. Texas provided estimates for 2002 non-road 

emissions in lieu of the CENRAP prepared inventory. WRAP provided non-road inventories 

divided between California and non-California seasonal inventories, further subdivided into 

aircraft, locomotives, shipping, and all other non-road mobile sources (Pollack et al., 2006). Note 

that the California Air Resources Board uses their own OFFROAD model for California non-
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_2_Emissions1.doc 2-24 

road emissions, whereas the EPA NONROAD model is used for the rest of the states (with the 

exception of locomotives, aircraft and shipping). With these data WRAP also provided temporal 

adjustments to apply to the inventories to split them between weekday and weekend emissions. 

We used these weekday/weekend splits to derive new weekly temporal profiles for the WRAP 

sources. The MRPO base K monthly non-road inventories were obtained from MRPO in NIF 

format and were converted to SMOKE format by Wendy Vit of the Missouri DNR. The VISTAS 

Base G and MANE-VU non-road mobile inventories consisted of annual county-level data 

(Pechan and CEP, 2005c). We received these inventories directly from the respective RPO 

inventory representatives. We received the Canadian 2000 inventory version 2 from the U.S. 

EPA EFIG (EPA, 2005d). For Mexico we used the improved 1999 inventory available at 

http://www.epa.gov/ttn/chief/net/mexico.html. 

Along with adding the WRAP weekday/weekend emissions splits to the temporal allocation 

files, we also created temporal input files that apply a flat, uniform monthly profile to the 

monthly and seasonal non-road inventories. With the monthly and seasonal variability inherent 

in these inventories, we avoided applying redundant monthly profiles by splitting the inventories 

into seasonal/monthly and annual data. We applied the uniform monthly temporal profiles to the 

seasonal/monthly inventories and non-uniform monthly temporal profiles to the annual 

inventories. How the non-road emissions inventory data were split into those with 

monthly/seasonal emission and those with annual emissions is provided in Table 2-13. 

Table 2-13. Non-road mobile-source inventory temporal configuration. 

Region Source Temporal Coverage 

WRAP (non-CA) Non-road mobile Seasonal 

WRAP (CA) Non-road mobile Seasonal 

WRAP Aircraft Seasonal 

WRAP Locomotive Annual 

WRAP In-port and near-shore shipping Annual 

CENRAP All non-road Annual 

CENRAP, IA Non road Ag. Monthly 

VISTAS All non-road Annual 

MRPO and MN All non-road Monthly 

MANE-VU All non-road Annual 

Canada All non-road Annual 

Mexico All non-road Annual 

Iowa elected to use the CENRAP-sponsored inventory for all of the non-road categories except 

for the agricultural equipment categories provided in Table 2-14. For these agricultural 

equipment categories, Iowa elected to use the Midwest RPO Base K inventory because this 

inventory provided improvements to the temporal allocation of emissions for the agricultural 

sector. The Base K inventory includes monthly emissions. The monthly emissions are used in 

the SMOKE IDA files for modeling. 

Table 2-14. Non-road agricultural emissions categories where the MRPO Base K inventory was 

used instead of the CENRAP inventory in Iowa. 

 SCC SCC Description 

22600050xx Off-highway Vehicle Gasoline, 2-Stroke: Agricultural Equipment (2 SCCs); 

22650050xx Off-highway Vehicle Gasoline, 4-Stroke: Agricultural Equipment (11 SCCs); 

22670050xx LPG : Agricultural Equipment (3 SCCs); 

22680050xx CNG : Agricultural Equipment (3 SCCs); and 

22700050xx Off-highway Vehicle Diesel : Agricultural Equipment (11 SCCs). 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_2_Emissions1.doc 2-25 

Texas provided annual and daily emissions for CO, CO2, NOx, VOC, SO2, PM10-FIL, and 

PM25-FIL for several oil and gas field equipment non-road categories (Table 2-15). Texas 

provided authorization to change the pollutant codes from PM10-FIL to PM10-PRI and PM25-

FIL to PM25-PRI. 

Table 2-15. Non-road oil and gas development equipment categories that Texas provided 

emissions to be used instead of the CENRAP inventory. 

SCC SCC Description 

2265010010 Off-highway Vehicle Gasoline, 4-Stroke : Industrial Equipment: Other Oil Field Equipment; 

2268010010 CNG : Industrial Equipment : Other Oil Field Equipment; and 

2270010010 Off-highway Vehicle Diesel : Industrial Equipment : Other Oil Field Equipment 

Lancaster County Nebraska provided its own non-road inventory for SCC 2260000000 (Offhighway Vehicle Gasoline, 2-Stroke : 2-Stroke Gasoline except Rail and Marine: All). The 

CENRAP-sponsored inventories for SCCs starting with 226 in Lancaster County were removed 

to correct double-counting of emissions. This adjustment was made by Pechan for Base02b 

modeling. 

2.5.2 Emissions Processing 

We configured SMOKE to process all of the non-road mobile emissions inventory data as arealike inventories using spatial surrogates to grid the county-level emissions. As the WRAP 

inventories contained pre-computed PM emissions, we did not have to use SMOKE to compute 

coarse mass PM (PMC). The WRAP non-road mobile inventories represented seven-day average 

emissions (different from the area inventory, which represented weekday average emissions). As 

actual weekly average emissions, we configured SMOKE to process them by setting 

WKDAY_NORMALIZE to “No.” For the rest of the non-road mobile inventories we processed 

the data as weekday average data by setting WKDAY_NORMALIZE to “Yes.” We also 

assumed that all of the VOC emissions in the inventories are ROG and used SMOKE to convert 

the VOC to TOG before converting the emissions into CB-IV speciation for the air quality 

models. We configured SMOKE to create MWSS temporal intermediates rather than daily 

temporal files because the non-road mobile sources do not use weekly temporal profiles that vary 

across the weekdays, but do have very different emissions on weekdays versus weekend days. 

We divided the non-road mobile emissions modeling based on whether the data were annual or 

seasonal/monthly inventories. This split facilitated the application of uniform monthly temporal 

profiles to the seasonal/monthly inventories. After processing the non-road emissions as two 

separate categories, non-road yearly and non-road monthly, we combined them with the rest of 

the emissions sectors to create model-ready emissions for CMAQ and CAMx. 

To QA the non-road mobile emissions we used the procedures in the CENRAP emissions 

modeling QAPP (Morris and Tonnesen, 2004) and Modeling Protocol (Morris et al., 2004a) and 

a suite of graphical summaries. We used tabulated summaries of the input data and SMOKE 

script settings to document the data and configuration of SMOKE for simulations. The graphical 

QA summaries include, for all emissions output species, daily spatial plots, daily time-series 

plots, and annual time-series plots. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/cenrap/qa_base02f36.shtml#nr
 

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2.5.3 Uncertainties and Recommendations 

We prepared non-road mobile emissions using a combination of inventories having different 

temporal resolutions and various forms of ancillary data. These different combinations of 

information may lead to inconsistencies in how these emissions are represented across the 

modeling domain. In addition, the Canadian inventories contain only province-level information 

and thus have low-resolution spatial and temporal profiles applied to them. The Mexican nonroad emissions are deficient in the number of different SCCs contained in the inventory and the 

availability of spatial surrogates that are applicable to non-road mobile sources. Improvements to 

the temporal profiles and spatial surrogates could provide a more consistent approach to 

representing the non-road emissions across the entire modeling domain. 

2.6 Biogenic Sources 

Biogenic emissions data for SMOKE consist of input files to the BEIS3 model (EPA, 2004a). 

BEIS3 is a system integrated into SMOKE for deriving emissions estimates of biogenic gasphase pollutants from land use information, emissions factors for different plant species, and 

hourly, gridded meteorology data. The results of BEIS3 modeling are hourly, gridded emissions 

fluxes formatted for input to CMAQ or CAMx. This section describes the sources of the BEIS3 

input data that we used for the Typ02G and Base18G emissions, how we modeled these data and 

the types of QA that were performed to ensure that SMOKE processed the data as expected. 

2.6.1 Data Sources 

The BELD3 land use data and biogenic emissions factors that were developed during the WRAP 

preliminary 2002 modeling were used for the CENRAP biogenic emissions modeling (Tonnesen 

et al., 2005). These data included BELD3 1-km resolution land use estimates and version 0.98 of 

the BELD emissions factors. Since the WRAP and CENRAP use the same 36 km Inter-RPO 

continental U.S. modeling domain, CENRAP was able to leverage of the WRAP work performed 

previously. 

2.6.2 Emissions Processing 

We used BEIS3.12 integrated in SMOKE to prepare emissions for the simulations. Most of the 

preparation for the biogenic emissions processing was completed during the preliminary 2002 

modeling (Morris et al., 2005). As the modeling domains did not change from the preliminary 

2002 to the final modeling, we re-used the gridded land use data and vegetation emissions factors 

that we prepared for the preliminary simulations. 

To QA the biogenic emissions, we used the CENRAP emissions modeling QAPP (Morris and 

Tonnesen, 2004) and Modeling Protocol (Morris et al., 2004a) and a suite of graphical 

summaries. We used tabulated summaries of the input data and SMOKE script settings to 

document the data and configuration of SMOKE for simulation Base02b. The graphical QA 

summaries include, for all emissions output species, daily spatial plots, daily time-series plots, 

and annual time-series plots. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/cenrap/qa_base02b36.shtml#b3
 

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2.6.3 Uncertainties and Recommendations 

The use of newer versions of BEIS (BEIS3.13) and the new MEGAN biogenic emissions models 

should be considered in future modeling. 

2.7 Fire Emissions 

Fire emissions data for SMOKE have traditionally been represented as county-level area-source 

inventories that were placed in only the first vertical model layer. We advanced the 

representation of fire emissions for air quality modeling by preparing portions of the inventory 

data as point sources with specific latitude-longitude coordinates for each fire centroid and precomputed plume rise parameters that were derived from individual fire characteristics. These 

new inventories were based on the fire data products prepared by a CENRAP emission 

contractor (Reid et al., 2004b) and modified by the project team to be properly modeled as point 

sources. These data consist of annual, daily, and hourly IDA-formatted emissions inventory files 

and ancillary data for allocating the inventories in space, time, and to the Carbon Bond-IV 

chemistry mechanism used in CMAQ and CAMx. This section describes where we obtained 

these data, how we modeled them, and the types of QA performed to ensure that SMOKE 

processed the fire emissions data as expected. 

2.7.1 Data Sources 

The fire inventories in the Typ02G emissions inventory were held constant through Base18G. 

We used actual 2002 fire data developed by the RPOs for the U.S., version 2 of the year 2000 

Canadian inventory fire data, and actual 2002 fire data for Ontario, Canada. The inventories used 

consisted of both area and point source data for the U.S., Canada, and Mexico. Sonoma 

Technology, Inc. provided the fire emissions for the CENRAP states (Reid et al., 2004b). Air 

Sciences provided us with the WRAP inventories divided among six different fire categories: 

wildfires, agricultural fires, wildland fire use, natural prescribed, anthropogenic prescribed, and 

non-Federal rangeland fires (Air Sciences, 2007a). These inventories consisted of annual, daily, 

and hourly IDA-formatted files with information on daily emissions totals and hourly plume 

characteristics for each fire. We received similar fire emission inventories for the other RPOS 

(Air Sciences, 2007b). We modeled these sources with the rest of the stationary-area-source 

sector. 

CENRAP received data for 54 fires that occurred in Ontario during the year 2002. Information 

on the data code abbreviations, data definitions, and data units used in the raw data files was 

obtained from Mr. Rob Luik (Data Management Specialist) at the Ontario Ministry of Natural 

Resources (Rob.Luik@MNR. gov.on.ca). Emissions for each fire were estimated using the 

Emission Production Model (EPM)/CONSUME within the BlueSky framework. A fire 

identification code is needed to track individual fires throughout the processing. The unique fire 

identification code was created for each fire by concatenating the FIRE_NUMBER and 

CUR_DIST fields of the original data. The fire identification code also contains the FIPS code 

of the fire; this information is not used by BlueSky but is needed by BlueSky2Inv, the utility 

program that converts the BlueSky output to the SMOKE inventory format. The FIPS code 

135000 was used for all fires with longitudes east of –90°, and FIPS code 135059 was used for 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_2_Emissions1.doc 2-28 

fires west of –90°. These FIPS codes were used to ensure that the fires would be assigned the 

correct time zones in later SMOKE processing. Some of the dates provided in the original data 

included hourly information. In all cases, the hourly information was not used leaving all data at 

a daily resolution. 

2.7.2 Emissions Processing 

SMOKE is instrumented to distribute point-source-formatted fire inventories to the vertical 

model layers either by using a pre-computed plume rise approach or by computing the plume rise 

dynamically using actual 2002 meteorology. We applied both approaches for modeling pointsource fire emissions in simulation Typ02G. For the pre-computed plume rise approach, 

SMOKE reads an annual inventory file with information on fire locations, a daily inventory file 

with daily emission totals for each fire, and an hourly inventory file with hourly plume bottom, 

plume top, and layer 1 fractions for each fire. SMOKE uses this information to locate the fires on 

the horizontal model grid and to distribute the plume of each fire vertically to the model layers. 

Because some of these fires have plumes that reach the model top, we set the number of 

emissions layers for processing these inventories to the full 19 layers of the meteorology. We 

applied this approach to the point-source fires for the WRAP, CENRAP and VISTAS regions. 

The alternative plume rise approach uses information on fuel loading and the heat flux of the 

fires to distribute the fires vertically to the model layers. The data are provided to SMOKE in the 

form of an annual inventory with information on fire locations and a daily inventory with daily 

emission totals for each fire, daily heat flux, and daily fuel loading. We applied this approach to 

the point-source fires for Ontario, Canada. 

All of the point-source fires used diurnal temporal profiles and speciation profiles for VOC and 

PM2.5 developed by Air Sciences (2007a) during the preliminary 2002 modeling (Morris et al., 

2005). 

We modeled the area-source fires for U.S. and Canada as standard stationary area sources. We 

applied monthly temporal profiles provided by RPOs, flat weekly temporal profiles, and the 

diurnal profiles developed by Air Sciences for WRAP fires (Air Sciences, 2007a), and for the 

rest of the RPOs we used diurnal profiles that were provided by them (Air Sciences, 2007b). We 

used the forestland area surrogate to distribute these emissions from the county or province level 

in the inventories to the model grid cells. 

To QA the fire emissions, we used the procedure in the CENRAP emissions modeling QA 

protocol (Environ, 2004) and a suite of graphical summaries. We used tabulated summaries of 

the input data and SMOKE script settings to document the data and configuration of SMOKE for 

simulation Typ02G. The graphical QA summaries include, for all emissions output species, daily 

spatial plots, daily time-series plots, annual time-series plots, and vertical profiles. These QA 

graphics are available at: http://pah.cert.ucr.edu/aqm/cenrap/qa_typ02g36.shtml. 

2.7.3 Uncertainties and Recommendations 

We used forestland spatial surrogates to distribute these county level (province level for Canada) 

data to the model grid. Using spatial surrogates to locate fires is a crude approach that results in 

the artificial smearing of the emissions over too large an area. This issue can be remedied by 
 

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moving to a point-source approach for representing these fires, similar to the approach used by 

Air Sciences for preparing the WRAP fire inventories.

2.8 Dust Emissions 

Dust emissions data for SMOKE have traditionally taken the form of county-level stationaryarea-source inventories. As these emissions are correlated to meteorology, land use, and 

vegetative cover, we made several changes to how dust emissions are simulated by SMOKE to 

take these parameters into consideration. This section describes where we obtained data for 

windblown, fugitive, and road dust sources, how we modeled them, and the types of QA 

performed to ensure that SMOKE processed the data as expected. 

2.8.1 Data Sources 

For the fugitive dust and road dust inventories in the Typ02G emission scenario, we used actual 

2002 data developed by the RPOs for the U.S., version 2 of the year 2000 Canadian inventory, 

and the BRAVO 1999 Mexican inventory. We extracted the fugitive dust inventories from the 

stationary-area inventories for each of the RPOs, Mexico, and Canada. Before modeling these 

data we further divided them into construction/mining sources and agricultural sources. We 

defined the fugitive dust sources in the Base02f modeling based on guidance provided by EPA 

(2004b). WRAP provide road dust emission inventories (Pollack et al., 2006). For the rest of the 

RPOs and Canada, we extracted the road dust SCCs from the stationary-area-source inventories. 

The BRAVO 1999 Mexico inventory did not contain any road dust SCCs. Table 2-16 lists the 

SCCs for the various fugitive and road dust sources that we modeled in the Base02f and Typ02G 

inventories. We applied near-source capture transport factors that are based on county-level 

vegetative cover to the fugitive and road dust inventories to prepare them for input to the air 

quality models. 

For windblown dust, we used gridded emissions prepared outside of SMOKE using a land use 

and meteorology-based model developed under funding from the WRAP by ENVIRON and UCRiverside (Mansell, 2005; Mansell et al., 2005). 

Table 2-16. Fugitive and road dust SCCs. 

Dust Category SCCs 

Fugitive dust (construction and mining) 2275085000, 2311000000, 2311010000, 2311010070, 

2311020000, 2311030000, 2325000000, 2305070000, 

2530000020, 2530000100, 2530000120 

Fugitive dust (agricultural) 2801000003, 2801000005, 2801000008, 2805001000 

Road dust 2294000000, 2296000000 

2.8.2 Emissions Processing 

We modeled the fugitive and road dust inventories through SMOKE using an area-source 

approach. We modeled these data on the assumption that they represented weekday, rather than 

seven-day week, emissions and thus used the SMOKE setting WKDAY_NORMALIZE to 

convert the data to a seven-day average. We configured SMOKE to compute PMC during the 
 

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processing as (PM10 - PM2.5). Usually the records with dust do not include any other pollutants 

such as VOC, and NOx. For the few records that did include pollutants other than the PM we 

split the records where the PMs processed with dust and the non PMs processed with the area. 

We configured SMOKE to create MWSS temporal intermediates rather than daily temporal files 

because the dust sources do not use weekly temporal profiles that vary across the weekdays. 

As noted above, we used SMOKE to apply near-source transport factors to the raw fugitive and 

road dust inventories to prepare them for input to the air quality models. We used U.S. transport 

factors from work done by Pace (2005) and a 2001 land use/land cover database to develop a 

SMOKE input file of county and SCC-based transport factors for the U.S., Canada, and Mexico. 

We applied these factors to create a new set of inventories adjusted for these transport factors for 

all regions except VISTAS; the VISTAS dust sources that we received already had the transport 

factors applied to them. 

We calculated the windblown dust emissions outside of SMOKE using an internally developed, 

process-based model. By “process-based” we refer to an emissions model that integrates 

information about the processes that lead to the emissions of interest, in this case windblown 

dust. The process-based windblown dust model developed by the WRAP considers wind speeds, 

precipitation history, and soil types to derive gridded dust fluxes resulting from wind 

disturbances for the modeling domain. More information on this model, its modes of operation, 

and the configuration used for simulation Base02a are available in Mansell et al. (2005). 

To QA the fire emissions, we used the procedures in the CENRAP emissions modeling QAPP 

(Morris and Tonnesen, 2004) and Modeling Protocol (Morris et al., 2004a) and a suite of 

graphical summaries. We used tabulated summaries of the input data and SMOKE script settings 

to document the data and configuration of SMOKE for Base02f emissions. The graphical QA 

summaries include, for all emissions output species, daily spatial plots, daily time-series plots, 

and annual time-series plots. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/cenrap/qa_base02f36.shtml#fd for fugitive dust, 

http://pah.cert.ucr.edu/aqm/cenrap/qa_base02f36.shtml#rd for road dust, and 

http://pah.cert.ucr.edu/aqm/cenrap/qa_base02b36.shtml#wbd for windblown dust. 

2.8.3 Uncertainties and Recommendations 

There are several improvements that should be made to the dust emissions modeling in future 

simulations. We will expand the list of fugitive dust SCCs that we extract from the stationaryarea-source inventories for application of transport factors. This expanded list is based on recent 

work by EPA (2004b). We will also explore improvements to the assumptions that we used for 

generating emissions with the WRAP windblown dust model. Areas of improvement in the 

windblown dust model include refinements to the land use data and soil characteristics, 

additional information about agricultural activities in the WRAP and CENRAP regions, detailed 

model evaluation on targeted windblown dust case studies, and the application of snow-cover 

and vegetative transport factors to these emissions (Mansell et al., 2005). 

2.9 Ammonia Emissions 

Ammonia (NH3) emissions from agricultural activities are a major source of ammonia and are 

dependent on many different environmental parameters, such as meteorology, crop and soil 
 

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types, and land use. CENRAP developed NH3 emissions for the CENRAP states (Pechan and 

CEP, 2005e). Ammonia emissions were estimated for 13 source categories using the Carnegie 

Mellon University (CMU) model and supplemental technical work; 80% of technical work was 

dedicated to improving emissions estimates for two source categories—livestock production and 

fertilizer use. For these two categories, as well as biogenic sources, improvements were made to 

the activity data and/or emission factors used by the CMU model. For four other source 

categories (industrial point sources, landfills, ammonia refrigeration, and non-road mobile 

sources), emissions estimates were prepared independently of the CMU model, and for the 

remaining six source categories (publicly owned treatment works, wildfires, domestic animals, 

wild animals, human respiration, and on-road mobile sources), emissions estimates were derived 

by running the CMU model with no alterations.

CENRAP NH3 model emissions estimates were combined with data provided by the other RPOs 

to represent agricultural NH3 emissions in simulations Typ02G and Base18G. 

2.9.1 Data Sources 

The WRAP provided NH3 emissions using the WRAP NH3 model (Mansell et al, 2005) that 

generated emissions for the following sectors: domestic sources, wild animals, fertilizers, soils, 

and livestock. MWRPO provided monthly IDA-formatted inventories reflective of base K to 

CENRAP that they produced from process-based models of their own, along with temporal 

profiles and spatial cross-reference information for these sources. Iowa elected to use the 

MWRPO estimates of NH3 emissions for fertilizer application, livestock, and wastewater 

treatment or SCC 28017XXXXX, 28050XXXXX, and 2630020000 respectively. Minnesota 

reviewed the MWRPO inventory and chose to move forward with the CENRAP developed data 

set. The rest of the U.S., Canada, and Mexico had agricultural NH3 emissions contained within 

their annual stationary-area-source inventories. 

2.9.2 Emissions Processing 

The WRAP NH3 emissions were processed outside of SMOKE using the WRAP NH3 model and 

provided to CENRAP as gridded, hourly emissions in network common data form (NetCDF) 

files. CENRAP and MWRPO provided monthly IDA-formatted, county-level NH3 inventories 

that were developed separately with process-based models. We modeled these emissions like 

area sources with SMOKE, applying the temporal profiles and the spatial cross-referencing 

developed for CENRAP that we received from the MWRPO. The agricultural NH3 emissions 

for the rest of the RPOs, Canada, and Mexico are contained within their stationary-area 

inventories. We applied the SMOKE default temporal profiles and spatial surrogates to all nonprocess-based NH3 emissions. 

To QA the NH3 emissions, we used the procedures in the CENRAP modeling QAPP (Morris and 

Tonnesen, 2004) and Modeling Protocol (Morris et al., 2004a) and a suite of graphical 

summaries. We used tabulated summaries of the input data and SMOKE script settings to 

document the data and configuration of SMOKE for simulations Typ02G and Base18G. The 

graphical QA summaries include, for all emissions output species, daily spatial plots, daily timeseries plots, and annual time-series plots. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/cenrap/index.shtml
 

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2.9.3 Uncertainties and Recommendations 

Like the other emissions categories that have traditionally been represented as stationary area 

sources, the agricultural NH3 emissions sector is affected by interregional inconsistencies in the 

way these emissions are represented. 

During the QA of the Base02a emissions, the WRAP discovered a problem with their soil NH3

estimates. The emission factor for soil NH3 that were used in developing these data produced too 

high an emission estimate from this sector. For simulations Base02B through Typ02G, we 

therefore removed the soil NH3 sector completely from the WRAP domain. In future simulations 

we will include these emissions with a revised emission factor for NH3 emissions from soils. 

2.10 Oil and Gas Emissions 

Emissions from oil and gas development activities have been poorly characterized in the past. 

Simulations These emissions have been sporadically reported by some states in their stationaryarea-source inventories, but for the most part were missing from our preliminary modeling. In 

the Typ02G and Base18G simulations, significant effort was made to better represent oil and gas 

production emissions explicitly as both area and point sources. 

2.10.1 Data Sources 

Emissions from oil and gas production activities for the CENRAP states were included with the 

other CENRAP state emission source categories (Pechan and CEP, 2005e). We received oil and 

gas production emissions inventories for the WRAP states and for tribal lands in the WRAP 

region as stationary-area-source and stationary-point-source IDA-formatted inventories. ERG, 

Inc. provided the point-source inventories with the rest of the stationary-point data (ERG, 

2006a). ENVIRON provided the area-source oil and gas inventories for non-CA WRAP states 

and for tribal lands in the WRAP region, along with spatial surrogates for allocating these data to 

the model grid (Russell and Pollack. 2005). Oil and gas production emissions data for outside of 

the WRAP region are contained in the stationary-area inventories. 

2.10.2 Emissions Processing 

We modeled the WRAP point-source oil and gas production emissions in combination with the 

rest of the stationary-point-source emissions. We modeled the WRAP area-source oil and gas 

production emissions explicitly as a separate category that included WRAP and tribal 

inventories. These data represent weekly average emissions and did not require any 

renormalization within SMOKE. We used spatial surrogates generated by ENVIRON to allocate 

these annual county-level emissions to the model grid. For all oil and gas emissions, we applied 

flat temporal profiles to create hourly inputs to CMAQ and CAMx. 

2.10.3 Uncertainties and Recommendations 

In future 2002 modeling California oil and gas production emissions should be replaced with 

revised data provided by the California Air Resources Board (CARB). In addition, WRAP has 
 

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updated their oil and gas production inventory for the base and future years in a Phase II work 

effort that substantially improved the emissions inventory estimates (Bar-Ilan et al., 2007). 

2.11 MMS Off-shore Gulf of Mexico Emissions 

Offshore area point source emissions include emissions in the Gulf of Mexico and off the coast 

of California that are associated with oil and gas drilling platforms. 

2.11.1 Data Sources 

We obtained year 2000 IDA-formatted point-source inventories for oil and gas platforms in the 

Gulf of Mexico from the Minerals Management Service (MMS) web site: 

http://www.gomr.mms.gov/homepg/regulate/environ/airquality/gulfwide_emission_inventory/20

00GulfwideEmissionInventory.html

We combined these with point-source data for coastal California provided to us by CARB during 

the preliminary 2002 modeling. We also obtained gridded area source emissions for platforms in 

the Gulf of Mexico from the MMS that we converted to the CENRAP 36-km model grid. 

The 2000 MMS Gulf wide Emission Inventory was updated as of June 2006 to account for a 

change in vessel emissions in the non-point source (non-platform) database file. The point 

source (platform) emission inventory database file has not changed from the original version. 

Area source emissions from offshore activities in the Gulf of Mexico were developed from the 

latest estimates provided by the Minerals Management Service (MMS). The MMS inventory 

includes both platform and non-platform sources. The non-platform area source emissions 

estimates are spatially allocated to lease blocks and protraction units throughout the Gulf of 

Mexico. Temporal and spatial allocation cross-reference data were developed from the MMS 

inventory data and formatted for input to the SMOKE emissions model by Carolina 

Environmental Programs. These data were provided to the CENRAP emissions modeling team 

for implementation within SMOKE. The spatial allocation surrogates were provided for 4-km 

grid cells. The UCR team used these surrogates and developed surrogates for 36-km grid cells. 

Because these data are references to lease blocks/protraction units, rather than counties, this 

source category was processed separately form all other emissions using a customized reference 

data and SMOKE run scripts.

We modeled the offshore point and area sources as separate categories in the simulations. We 

used SMOKE to locate the offshore point sources on the model grid and to vertically allocate 

them into 15 model layers. 

To QA the offshore platform emissions, we used the procedures in the CENRAP modeling 

QAPP (Morris and Tonnesen, 2004) and Modeling Protocol (Morris et al., 20042) and a suite of 

graphical summaries. We used tabulated summaries of the input data and SMOKE script settings 

to document the data and configuration of SMOKE for simulation Base02a. The graphical QA 

summaries include, for all emissions output species, daily spatial plots, daily time-series plots, 

and annual time-series plots. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/cenrap/index.shtml for the point and area sources. 
 

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2.11.2 Uncertainties and Recommendations 

While the MMS data that we used were an improvement over previously modeled Gulf of 

Mexico platform inventories, the data were developed for a different modeling application that 

covered only the extreme northwestern portion of the Gulf, so they are missing large areas of the 

region of the Gulf that contain drilling platforms. The California offshore inventory represents an 

initial attempt at compiling an emission inventory for this area and contains very few sources. 

Future simulations will focus on improving these emissions by expanding the coverage of the 

offshore platform inventories for both the Gulf of Mexico and the Pacific Coast. 

2.12 Off-shore Shipping Emissions 

Emission inventory development for regional- and continental-scale air quality modeling has 

historically neglected offshore emissions sources beyond 25 miles offshore. Concern over the 

environmental effects of commercial shipping emissions in the Pacific on the coastal states in the 

WRAP region led to the development of a commercial marine shipping inventory for the Pacific. 

This inventory of off-shore marine vessels emissions made a substantial difference in some of 

the coastal western PM estimates (e.g., SO4). VISTAS developed an off-shore marine vessels 

inventory for the entire modeling domain that included the Pacific and Atlantic Oceans and the 

Gulf Of Mexico. For Typ02G and Base18G emission inventories CENRAP adopted the offshore 

shipping inventories developed by VISTAS. 

2.12.1 Data Sources 

Initially we obtained gridded annual commercial marine shipping emissions for the Pacific on 

the 36-km model grid from WRAP for inclusion in CENRAP simulations in the Base F modeling 

(Pollack et al., 2006). The commercial marine inventory contains all of the criteria pollutants 

contained in the non-road mobile-source inventory: CO, NOx, VOC, NH3, SO2, PM10, and PM2.5. 

This inventory was subsequently updated in the Typ02G and Base18G modeling with the 

VISTAS off-shore commercial marine emissions inventory that covered the Gulf of Mexico and 

the Atlantic and Pacific Oceans and was based on the EPA/ARB SOx Emissions Control Area 

(SECA) program. Dr. James Corbett (University of Delaware) analyzed off-shore marine vessel 

data and worked with ENVIRON/ICF to convert to gridded emissions for the SECA grid. 

ENVIRON then provided SO2, NOX, PM and VOC emissions for the RPO 36-km grid. 

2.12.2 Emissions Processing 

The commercial marine shipping inventory was not processed through SMOKE. VISTAS 

provided the data to the as gridded text files on the 36-km model grid. These data were 

reformatted to the NetCDF CMAQ input format with a utility developed by UCR. The VOC 

inventory was converted to CB-IV speciation and the NOx and PM2.5 inventory pollutants to 

CMAQ input species with SMOKE chemical profiles for commercial shipping sources. No 

temporal adjustments were applied to these emissions; they use uniform monthly, daily, and 

diurnal profiles. An SCC for commercial marine vessels within the MMS inventory (SCC 

CM80002200) was accounted for in the commercial marine inventory developed for VISTAS. 

The duplicate emissions were removed from the MMS inventory prior to processing emissions 
 

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for Base G simulations. The duplicated emissions amounted to 19,000 TPY of NOX and 3,184 

TPY of SO2. For simulation Typ02G and Base18G we received binary netCDF file from 

ENVIRON for one day and that day was used for every day of the year. 

To QA the commercial marine shipping emissions, we used the procedures in the CENRAP 

modeling QAPP (Morris and Tonnesen, 2004) and Modeling Protocol (Morris et al., 2004a) and 

a suite of graphical summaries. The graphical QA summaries include, for all emissions output 

species, daily spatial plots, daily time-series plots, and annual time-series plots. These QA 

graphics are available at http://pah.cert.ucr.edu/aqm/cenrap/index.shtml. 

2.12.3 Uncertainties and Recommendations 

As a first attempt at representing shipping emissions in the Pacific in international waters, the 

WRAP and VISTAS 2002 commercial shipping inventory is a breakthrough in a historically 

neglected emissions category. As the RPOs evaluate the effects of these emissions on the air 

quality modeling, we anticipate that there will be refinements to the temporal profiles and to the 

vertical allocation of the emissions. Many of the stacks of large commercial ships contained in 

this inventory extend vertically above the first model layer. Future versions of this inventory 

should use higher-resolution temporal adjustments and should allocate the emissions to the 

appropriate model layers. Off-shore marine shipping activity is projected to increase. However, 

there are also the potential for emission controls on this source category (e.g., SECA program). 

Given these two off setting activities, the 2002 off-shore marine shipping emissions were 

assumed to be unchanged going from 2002 to 2018. Better estimates of 2018 marine emissions 

are being developed that should be considered in future modeling activities. 

2.13 2018 Growth and Control 

Base18G was based on grown inventories assuming on-the-books control strategies. CENRAP 

contracted with Pechan to deliver growth and control data for CENRAP and to consolidate 

growth and control information for other RPOs where available (Pechan, 2005d). The data are 

applicable to all source categories and pollutants included in the CENRAP 2002 emission 

inventory. This includes the following pollutants: sulfur oxides (SOx), oxides of nitrogen (NOx), 

volatile organic compounds (VOC), carbon monoxide (CO), ammonia (NH3), and primary PM10 

and PM2.5. Some source categories were held constant between 2002 and 2018 because either 

stagnant growth was deemed appropriate or insufficient data was available to adequately project 

future growth or controls. These source categories include the following: 

• Wind Blown Dust from non-agricultural land use categories. 

• Emissions from wildfires. 

• Emissions from Mexico. 

• Global transport sources (i.e., the 2002 GEOS-CHEM boundary conditions). 

2.13.1 Data Sources 

CENRAP contracted with Pechan to provide growth and control factors to be applied with 

SMOKE for the CENRAP region (Pechan, 2005d). These growth and control parameters were 

based on growth estimates derived from EGAS 5.0 and control estimates assumed for 
 

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implementation of federal regulations and on-the-books state and local control programs. 

Emissions projections for electric generating units were developed for the RPOs with the 

Integrated Planning Model (IPM). The RPO 2.1.9 IPM results were subsequently modified by 

VISTAS, MRPO and CENRAP to reflect planned new construction and controls. The WRAP 

provided 2018 EGU estimates developed in coordination with State and Industry stakeholders. 

VISTAS, MWRPO and the WRAP provided emissions for 2018, having applied growth and 

control factors outside of SMOKE processing. EPA provided SMOKE processed emissions, 

applying both growth and controls, for Canada for the year 2020. These emissions were 

provided on the RPO 36-km grid. However, emissions were inexplicably processed for an 

alternative vertical structure. Alpine Geophysics, under contract to VISTAS reallocated the 

emissions through the vertical layers to more accurately reflect the vertical structure applied 

uniformly by the RPOs. The modified data was obtained directly from Alpine Geophysics. 

Emissions from Mexico were held constant between the inventory year 1999 and modeled 2002 

and 2018. Improvements to the Mexican inventory have been continuously made between 

generation of the original BRAVO inventory and the present improved 1999 inventory. 

However, given the continued uncertainties in the improved inventory, no future year projections 

where attempted by CENRAP. 

2.13.2 Emissions Processing 

Growth and control factors developed by Pechan (2005d) for Arkansas did not match the final 

delivered inventory for Arkansas. Arkansas underwent major revisions to point and facility IDs 

in mid-2005. These updates were not available by the delivery date of the growth and control 

parameters. In coordination with Arkansas, a cross-walk was developed to correct the point and 

facility IDs. 

The assumptions that went into the development of controls for engines covered under the RICE 

MACT were not consistent with the final rule. Rule penetration values for CENRAP states were 

adjusted to more accurately reflect the impact of the final rule. 

The impact of the refinery global settlements was not incorporated into CENRAP modeling until 

the base G simulations. Control assumptions provided by EPA and referenced in EPA CAIR 

modeling were applied to the 2018 inventory. These reductions primarily impacted SO2

emissions; however, NOX reductions were applied in Oklahoma, Louisiana, and Minnesota. 

2.13.3 Uncertainties and Recommendations 

The impact of control programs is an area of uncertainty that will need continued review as the 

programs are implemented. Development of growth and control assumptions for Mexico will be 

necessary for continued refinement of the impact of international transport. CENRAP obtained 

estimates of increased prescribed burn activity for the Forest Service after processing of the base 

G simulations was underway. These estimates of increased activity should be reviewed for 

inclusion in future simulations. EPA developed 2020 estimates of Canadian emissions are 

assumed to include erroneous stack parameters previously addressed in the 2000 emissions 

processing. Further review of this data set is recommended. 
 

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2.14 2018 Base G C1 Control Sensitivity 

CENRAP conducted a control sensitivity evaluating the impact of point source reductions given 

a maximum dollar per ton control level. The intent of the control sensitivity was to generate 

information on the impact of possible control strategies in support of the consultation process. 

The strategies were grouped together under a common set of criteria and not specifically 

identified by the states. The results of the modeling were not intended to be prescriptive; instead, 

they were intended to be a starting point for control discussions that would require much greater 

refinement. 

2.14.1 Data Sources 

CENRAP contracted with Alpine Geophysics to provide an evaluation of possible additional 

controls for the 2018 CENRAP point source inventory. These controls were in addition to onthe-books and BART controls assumed in the development of Base18F and Base18G emission 

scenarios. Base18F IDA files were enhanced with additional information on base level controls. 

The enhanced dataset was then linked with the control data contained in the 2006 release of 

EPA’s AirControlNet software. Alpine developed cost curves for NOX and SO2 in 2005 dollars 

for the Base18F CENRAP point source inventory. Staff from Iowa DNR and Kansas DHE 

worked in conjunction to add area of influence data (Alpine Geophysics, 2006) and distance 

calculations to each Class I area in CENRAP. A variety of dollar per ton control levels were 

evaluated. CENRAP elected to base the sensitivity on a maximum control cost of $5,000 per 

ton. This selection was made with the understanding that the cost data under-represented the true 

cost of retrofit controls and did not take in to consideration more recent market fluctuations 

impacting costs of controls and construction. CENRAP refined the selection by applying 

controls to only those sources that met the criteria that the ratio of their emissions in tons per 

year to their distance to any Class I area in kilometers be less than 5. This distance weighting 

criteria allowed the sensitivity to focus on those sources with the greatest impact. Additional 

controls for other RPOs were not considered in this evaluation. 

2.14.2 Emissions Processing 

Sources considered for control were removed from the IDA files. Growth and control 

assumptions were applied outside of SMOKE and delivered to UCR as 2018 emissions. Stack 

parameter changes as a result of additional controls were not considered in this analysis. 

2.14.3 Uncertainties and Recommendations 

Given uncertainties in control costs more refined analyses should include an evaluation of 

retrofit control costs under present values. 
 

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2.15 Emissions Summaries 

Appendix B provides details on the source of the emission files used in the CENRAP Typ02G 

and Base18G modeling. Also in Appendix B are sample emission summary plots, additional 

plots are available on the CENREAP modeling website: 

http://pah.cert.ucr.edu/aqm/cenrap/emissions.shtml. 

CENRAP has contracted with E.H. Pechan and Associates to provide emissions summaries used 

in the final Typ02G and Base18G modeling in Excel spreadsheets and in an Access database that 

are available on the CENRAP website (http://www.cenrap.org/projects.asp#). Figures 2-3 

through 2-9 display the, respectively, SO2, NOx, VOC, PM2.5, PM10, NH3 and CO 

anthropogenic emissions for the CENRAP states and the Typ02G and Base18G emission 

scenarios. Emissions are broken down by major source sector. For the state of Texas the 

emissions are broken by three groups, northeast Texas, southeast Texas and remainder of Texas 

(west Texas). 

For most states, EGUs are the largest contributor to SO2 emissions (Figure 2-3). As EGU SO2 

emissions are generally projected to be reduced in the future, most states show a reduction in 

total SO2 emissions from 2002 to 2018. One exception to this is Louisiana for which non-EGU 

point source SO2 emissions are greater than for EGU and are projected to increase from 2002 to 

2018. The reasons for these increases are unclear, but the growth factors for non-EGU points 

should be examined more carefully. 

NOx emissions are fairly evenly distributed across non-EGU point, EGU point, non-road mobile, 

on-road mobile and area sources for the 2002 Typ02G emissions scenario (Figure 2-4). In 2018, 

the contributions of on-road mobile source NOx emissions is reduced dramatically, with some 

states also showing reductions in EGU NOx emissions as well, resulting in all states exhibiting 

lower NOx emissions in 2018 than 2002. 

VOC emissions are dominated by area, non-road mobile, on-road mobile and non-EGU point 

sources in both 2002 and 2018 (Figure 2-5). VOC emissions from on-road and non-road mobile 

source are projected to go down in the future, whereas VOC emissions from non-EGU point and, 

especially, area sources are projected to increase. Thus, whether a state’s total VOC emissions 

increase or decrease depends on the relative contributions of mobile versus area sources and the 

level of increase in area source VOC emissions. Note that the VOC emissions listed in Figure 

2-5 do not include biogenic VOC emissions that would be greater than the anthropogenic VOC 

emissions shown in Figure 2-5. Note that because biogenic VOC emissions are processed using 

the SMOKE/BEIS module on the 36 km grid, state-wide biogenic VOC emissions summaries are 

not readily available. 

Primary PM2.5 emissions are primarily from road dust and fugitive dust, and for some states fires 

(Figure 2-6). Kansas, Oklahoma, Louisiana and Texas all have large contributions from fires not 

seen in the other states. Road dust and fugitive dust are the most dominate source categories for 

coarse particulate as well (Figure 2-7). 

CENRAP developed a separate ammonia emissions for 13 categories using the CMU model 

including livestock and fertilizer that dominates the ammonia emissions across the CENRAP 
 

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states (Figure 2-8). Several states also have significant ammonia contributions from non-EGU 

point sources, whereas others do not. 

CO emissions are dominated by the on-road and non-road mobile source sectors (Figure 2-9). 

However, states with fires also see large CO contributions from them as well. On-road mobile 

source CO emissions are projected to go down substantially from 2002 to 2018, whereas the 

other source categories are flat. 

Annual SO2 Emissions by Source Sector

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

Arkansas-02

Arkansas-18

Iowa-02

Iowa-18

Kansas-02

Kansas-18

Louisiana-02Louisiana-18

Minnesota-02

Minnesota-18

Missouri-02Missouri-18

Nebraska-02

Nebraska-18

Oklahoma-02

Oklahoma-18

Texas-02-NE

Texas-18-NE

Texas-02-SE

Texas-18-SE

Texas-02-REST

Texas-18-REST

Tons per year

roaddust

point_fire

point_negu

point_egu

onroad

offroad

fug_dust

area_fire

area

ammonia

Figure 2-3. Summary of Typ02G and Base18G SO2 emissions by CENRAP state and major 

source sector (tons per year). 
 

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Annual NOX Emissions by Source Sector (tons)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

Arkansas-02

Arkansas-18

Iowa-02

Iowa-18

Kansas-02

Kansas-18

Louisiana-02

Louisiana-18

Minnesota-02

Minnesota-18

Missouri-02Missouri-18

Nebraska-02

Nebraska-18

Oklahoma-02

Oklahoma-18

Texas-02-NE

Texas-18-NE

Texas-02-SE

Texas-18-SE

Texas-02-REST

Texas-18-REST

Tons per year

roaddust

point_fire

point_negu

point_egu

onroad

offroad

fug_dust

area_fire

area

ammonia

Figure 2-4. Summary of Typ02G and Base18G NOx emissions by CENRAP state and major 

source sector (tons per year).

Annual VOC Emissions by Source Sector (tons)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

Arkansas-02

Arkansas-18

Iowa-02

Iowa-18

Kansas-02

Kansas-18

Louisiana-02

Louisiana-18

Minnesota-02

Minnesota-18

Missouri-02

Missouri-18

Nebraska-02

Nebraska-18

Oklahoma-02

Oklahoma-18

Texas-02-NE

Texas-18-NE

Texas-02-SE

Texas-18-SE

Texas-02-REST

Texas-18-REST

Tons per year

roaddust

point_fire

point_negu

point_egu

onroad

offroad

fug_dust

area_fire

area

Figure 2-5. Summary of Typ02G and Base18G VOC emissions by CENRAP state and major 

source sector (tons per year).
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_2_Emissions1.doc 2-41 

Annual PM25 Emissions by Source Sector (tons)

0

50,000

100,000

150,000

200,000

250,000

Arkansas-02

Arkansas-18

Iowa-02

Iowa-18

Kansas-02

Kansas-18

Louisiana-02

Louisiana-18

Minnesota-02

Minnesota-18

Missouri-02

Missouri-18

Nebraska-02

Nebraska-18

Oklahoma-02

Oklahoma-18

Texas-02-NE

Texas-18-NE

Texas-02-SE

Texas-18-SE

Texas-02-REST

Texas-18-REST

Tons per year

roaddust

point_fire

point_negu

point_egu

onroad

offroad

fug_dust

area_fire

area

ammonia

Figure 2-6. Summary of Typ02G and Base18G PM2.5 emissions by CENRAP state and major 

source sector (tons per year).

Annual PM10 Emissions by Source Sector (tons)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

Arkansas-02

Arkansas-18

Iowa-02

Iowa-18

Kansas-02

Kansas-18

Louisiana-02

Louisiana-18

Minnesota-02

Minnesota-18

Missouri-02

Missouri-18

Nebraska-02

Nebraska-18

Oklahoma-02

Oklahoma-18

Texas-02-NE

Texas-18-NE

Texas-02-SE

Texas-18-SE

Texas-02-REST

Texas-18-REST

Tons per year

roaddust

point_fire

point_negu

point_egu

onroad

offroad

fug_dust

area_fire

area

ammonia

Figure 2-7. Summary of Typ02G and Base18G PM10 emissions by CENRAP state and major 

source sector (tons per year).
 

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Annual NH3 Emissions by Source Sector (tons)

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

Arkansas-02

Arkansas-18

Iowa-02

Iowa-18

Kansas-02

Kansas-18

Louisiana-02

Louisiana-18

Minnesota-02

Minnesota-18

Missouri-02

Missouri-18

Nebraska-02

Nebraska-18

Oklahoma-02

Oklahoma-18

Texas-02-NE

Texas-18-NE

Texas-02-SE

Texas-18-SE

Texas-02-REST

Texas-18-REST

Tons per year

roaddust

point_fire

point_negu

point_egu

onroad

offroad

fug_dust

area_fire

area

ammonia

Figure 2-8. Summary of Typ02G and Base18G NH3 emissions by CENRAP state and major 

source sector (tons per year).

Annual CO Emissions by Source Sector (tons)

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

Arkansas-02

Arkansas-18

Iowa-02

Iowa-18

Kansas-02

Kansas-18

Louisiana-02

Louisiana-18

Minnesota-02

Minnesota-18

Missouri-02

Missouri-18

Nebraska-02

Nebraska-18

Oklahoma-02

Oklahoma-18

Texas-02-NE

Texas-18-NE

Texas-02-SE

Texas-18-SE

Texas-02-REST

Texas-18-REST

Tons per year

roaddust

point_fire

point_negu

point_egu

onroad

offroad

fug_dust

area_fire

area

ammonia

Figure 2-9. Summary of Typ02G and Base18G CO emissions by CENRAP state and major 

source sector (tons per year).
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-1 

3.0 MODEL PERFORMANCE EVALUATION 

In this Chapter we summarize the CMAQ model performance for the final 2002 36 km Base F 

base case simulation. Because the 2002 Base F CMAQ simulation produced nearly identical 

results in the U.S. as the final 2002 Base G simulation and limited resource availability, 

CENRAP elected not to redo the model evaluation for the 2002 Base G case. This model 

performance focuses on the ability of the model to predict PM species within the CENRAP 

region. Details on the model performance are provided in Appendix C. Previously we have 

documented model performance of interim versions of model base case simulations in reports 

(Morris et al., 2005) and presentations to the CENRAP Work Groups and POG (e.g., Morris et 

al., 2006a,b). 

3.1 Evaluation Methodology 

EPA’s integrated ozone, PM2.5 and regional haze modeling guidance calls for a comprehensive, 

multi-layered approach to model performance testing, consisting of the four major components: 

operational, diagnostic, mechanistic (or scientific) and probabilistic (EPA, 2007). The CMAQ 

model performance evaluation effort focused on the first two components, namely: 

• Operational Evaluation: Tests the ability of the model to estimate PM concentrations 

(both fine and coarse) and the components at PM10 and PM2.5 including the quantities 

used to characterize visibility (i.e., sulfate, nitrate, ammonium, organic carbon, elemental 

carbon, other PM2.5, and coarse matter (PM2.5-10). This evaluation examines whether the 

measurements are properly represented by the model predictions but does not necessarily 

ensure that the model is getting “the right answer for the right reason”; and 

• Diagnostic Evaluation: Tests the ability of the model to predict visibility and extinction, 

PM chemical composition including PM precursors (e.g., SOx, NOx, and NH3) and 

associated oxidants (e.g., ozone and nitric acid); PM size distribution; temporal variation; 

spatial variation; mass fluxes; and components of light extinction (i.e., scattering and 

absorption). 

In this final model performance evaluation for the 2002 Typical Base F CMAQ simulation, the 

operational evaluation has been given the greatest attention since this is the primary thrust of 

EPA’s modeling guidance. However, we have also examined certain diagnostic features dealing 

with the model’s ability to simulate sub-regional, monthly, diurnal, gas phase and aerosol 

concentration distributions. In the course of the CENRAP air quality modeling and other 

modeling processes, numerous diagnostic sensitivity tests were performed to investigate and 

improve model performance. Key diagnostic tests that were performed and the results are 

discussed on the CENRAP modeling website: http://pah.cert.ucr.edu/aqm/cenrap/index.shtml. 
 

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3.2 Ambient Air Quality Data used in the Evaluation 

The ground-level model evaluation database for 2002 was compiled by the modeling team using 

several routine and research-grade databases. The first is the routine gas-phase concentration 

measurements for ozone, SO2, NO2 and CO archived in EPA’s Aerometric Information Retrieval 

System (AIRS) Air Quality System (AQS) database. Other sources of observed information 

come from the various PM monitoring networks in the U.S. These include the Interagency 

Monitoring of Protected Visual Environments (IMPROVE); Clean Air Status and Trends 

Network (CASTNET); EPA Speciation Trends Network (STN) of PM2.5 species; and National 

Acid Deposition Program (NADP). During the course of the CENRAP modeling, the numerous 

base case simulations were evaluated across the continental U.S. (e.g., Morris et al., 2005). In 

this section and in Appendix C we focus our evaluation on model performance within the 

CENRAP region. 

3.2 Operational Model Evaluation Approach 

The CENRAP modeling databases will be used to develop the visibility State Implementation 

Plan (SIP) as required by the Regional Haze Rule (RHR). Accordingly, the primary focus of the 

operational evaluation in this report is on the six components of fine particulate (PM2.5) and 

coarse mass (PM2.5-10) within the CENRAP region that are used to characterize visibility at Class 

I areas: 

• Sulfate (SO4); 

• Particulate Nitrate (NO3); 

• Elemental Carbon (EC); 

• Organic Mass Carbon (OMC); 

• Other inorganic fine particulate (IP or Soil); and 

• Coarse Mass (CM). 

The model performance for ozone, precursors, and product species (e.g., SO4 , NO3, NH4 and 

HNO3) is also evaluated to build confidence that the modeling system is sufficiently reliable to 

project future-year visibility. 

3.3 Model Performance Goals and Criteria 

The issue of model performance goals for PM species is an area of ongoing research and debate. 

For ozone modeling, EPA has established performance goals for 1-hour ozone: normalized 

mean bias and gross error of #±15% and #35%, respectively (EPA, 1991). EPA’s draft fine 

particulate modeling guidance notes that performance goals for ozone should be viewed as upper 

bounds of model performance that PM models may not be able to always achieve and that we 

should demand better model performance for PM components that make up a larger fraction of 

the PM mass than those that are minor contributors (EPA, 2001). EPA’s final modeling 

guidance does not list any specific model performance goals for PM and visibility modeling and 

instead provides a summary of PM model performance across several historical applications that 

can be used for comparisons, if desired. Measuring PM species is not as precise as ozone 

monitoring. In fact, the uncertainty in measurement techniques for some PM species is likely to 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-3 

exceed the more stringent model performance goals, such as those for ozone. For example, 

recent comparisons of the PM species measurements using the IMPROVE and STN 

measurement technologies found uncertainties of approximately ∀20% (SO4) to ∀50% (EC) 

(Solomon et al., 2004). 

For the CENRAP modeling we have adopted three levels of model performance goals and 

criteria for bias and gross error as listed in Table 3-1. Note that we are not suggesting that these 

performance goals be adopted as guidance. Rather, we are just using them to frame and put the 

PM model performance into context and to facilitate model performance intercomparison across 

episodes, species, models and sensitivity tests. 

Table 3-1. Model performance goals and criteria used to assist in interpreting modeling results. 

Fractional 

Bias 

Fractional 

Gross 

Error Comment 

#∀15% #35% 

Ozone model performance goal for which PM model 

performance would be considered “good” – note that for 

many PM species measurement uncertainties may exceed 

this goal. 

#∀30% #50% 

Proposed PM model performance goal that we would hope 

each PM species could meet 

#∀60% #75% 

Proposed PM criteria above which indicates potential 

fundamental problems with the modeling system. 

As noted in EPA’s PM modeling guidance, less abundant PM species should have less stringent 

performance goals (EPA, 2001; 2007). Accordingly, we are also using performance goals that 

are a continuous function of average concentrations, as proposed by Dr. James Boylan at the 

Georgia Department of Natural Resources (GA DNR), that have the following features (Boylan, 

2004): 

• Asymptotically approaching proposed performance goals or criteria (i.e., the ∀30%/50% 

and ∀60%/75% bias/error levels listed in Table 3-1) when the mean of the observed 

concentrations are greater than 2.5 ug/m3. 

• Approaching 200% error and ∀200% bias when the mean of the observed concentrations 

are extremely small. 

Bias and error are plotted as a function of average concentrations. As the mean concentration 

approaches zero, the bias performance goal and criteria flare out to ∀200% creating a horn 

shape, hence the name “Bugle Plots”. Dr. Boylan has defined three Zones of model 

performance: Zone 1 meets the ∀30%/50% bias/error performance goal and is considered 

“good” model performance; Zone 2 lies between the ∀30%/50% performance goal and 

∀60%/75% performance criteria and is an area where concern for model performance is raised; 

and Zone 3 lies above the ∀60%/75% performance criteria and is an area of questionable model 

performance. 
 

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3.4 Key Measures of Model Performance 

Although we have generated numerous statistical performance measures (see Table C-2 in 

Appendix C) that are available on the CENRAP modeling website, when comparing model 

performance across months, subdomains, networks, grid resolution, models, studies, etc. it is 

useful to have a few key measurement statistics to be used to facilitate the comparisons. It is 

also useful to have a subset of months within the 2002 year that can represent the entire year so 

that a more focused evaluation can be conducted. We have found that the Mean Fractional Bias 

and Mean Fractional Gross Error appear to be the most consistent descriptive measure of model 

performance (Morris et al., 2004b; 2005). The Fractional Bias and Error are normalized by the 

average of the observed and predicted value (see Table C-2) because it provides descriptive 

power across different magnitudes of the model and observed concentrations and is bounded by 

-200% to +200%. This is in contrast to the normalized bias and error (as recommended for 

ozone performance goals, EPA, 1991) that is normalized by just the observed value so can “blow 

up” to infinity as the observed value approaches zero. In Appendix C we perform a focused 

evaluation of model performance for PM and gaseous species and four months of the 2002 year 

that are used to represent the seasonal variation in performance: 

• January 

• April 

• July 

• October 

Scatter plots of model predictions and observations for each PM species are presented for each of 

the four months along with performance statistics and predicted and observed time series plots at 

each CENRAP Class I area. Summary plots of monthly fractional bias and error are also 

presented. 

3.5 Operational Model Performance Evaluation 

A summary of the operational evaluation is presented below. Just the monthly fractional bias 

performance metrics for each PM species using bar charts and Bugle Plots are presented in this 

section. The reader is referred to Appendix C for the complete model performance evaluation. 

3.5.1 Sulfate (SO4) Model Performance 

Figure 3-1 compares the monthly SO4 fractional bias across the CENRAP region for the 

IMPROVE, STN and CASTNet monitoring networks. An underprediction bias is clearly evident 

the first 8-10 months of the year. This underestimation bias is greatest across the CASTNet 

network which persists throughout the year. The SO4 underprediction is not as severe for the 

STN network and it is minimal by August becoming a slight overprediction in September. For 

the IMPROVE network, the SO4 fractional bias is < ±20% for the first 2 and last 3 months of the 

year and ranges from -30% to -50% for the late Spring and Summer months. 

Figure 3-1 also includes a Bugle Plot of monthly SO4 fractional bias statistics (for Bugle Plot of 

fractional gross error see Appendix C) and compares them against the proposed PM model 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-5 

performance goal and criteria (see Table 3-1). For the STN network, SO4 model performance 

meets the proposed performance goal for all months. For the IMPROVE network, 

approximately half of the months achieve the proposed PM performance goal with the other half 

outside of the goal, but within the performance criteria. Across the CASTNet network, most 

months are outside of the proposed goal but are within the criteria. The CASTNet fractional bias 

for some months is right at the performance criteria (≤±60%). With the exception of two 

IMPROVE months, the monthly SO4 fractional bias performance statistics achieve the proposed 

PM model performance goal. 

3.5.2 Nitrate (NO3) Model Performance 

Monthly NO3 model performance across the CENRAP region is characterized by a summer 

underestimation and winter overestimation bias (Figure 3-2). The summer underestimation bias 

is more severe, exceeding -100%. Whereas, the winter overestimation bias is approximately 

50%. So based on statistics alone, it appears the summer underestimation bias is a bigger 

concern than the winter overestimation bias. However, the Bugle Plots in the bottom part of 

Figure 3-2 show that the summer underestimation bias occurs when NO3 is very low and is not 

an important component of PM and visibility impairment. These summer values occur in the 

flared horn part of the Bugle Plot and the summer NO3 performance, in most cases, achieves the 

model performance goal and always achieves the performance criteria. Whereas, the winter 

overstated NO3 performance for the most part doesn’t meet the performance goal and there are 

some months/networks that also don’t meet the performance criteria. 

3.5.3 Organic Matter Carbon (OMC) Model Performance 

The OMC monthly fractional bias across IMPROVE and STN sites in the CENRAP region are 

shown in Figure 3-3. The fractional bias for OMC at the IMPROVE sites is quite good 

throughout the year with values generally within ±20%, albeit with a slight winter overestimation 

and summer underestimation bias. At the urban STN sites, the model exhibits an 

underestimation bias throughout the year that ranges from -20% to -50%. The urban 

underestimation of OMC is a fairly common occurrence and suggests there may be missing 

sources of organic aerosol emissions in the modeling inventory. 

The good performance of the model for OMC at the IMPROVE sites is also reflected in the 

Bugle Plot (Figure 3-3, bottom) with the bias achieving the proposed PM model performance 

goal for all months of the year. At the STN sites, however, the OMC bias falls between the 

proposed PM model performance goal and criteria, with error right at the goal for most months. 
 

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Figure 3-1. Monthly fractional bias (%) for sulfate (SO4) across the CENRAP region for the 

CMAQ 2002 36 km Base F base case simulation. 
 

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Figure 3-2. Monthly fractional bias (%) for nitrate (NO3) across the CENRAP region for the 

CMAQ 2002 36 km Base F base case simulation. 
 

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Figure 3-3. Monthly fractional bias (%) for organic matter carbon (OMC) across the CENRAP 

region for the CMAQ 2002 36 km Base F base case simulation. 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-9 

3.5.4 Elemental Carbon (EC) Model Performance 

The monthly average bias for EC across the IMPROVE and STN monitors in the CENRAP 

region are shown in Figure 3-4. The STN network exhibits small fractional bias year round, 

whereas the IMPROVE monitoring network exhibits a large underprediction bias in the summer 

months (-40% to -70%) and much smaller bias in the winter. The Bugle Plot puts the EC 

performance in context. The low EC concentrations at the IMPROVE sites results in bias values 

in the horn of the Bugle Plot. Thus, EC bias achieves the proposed PM performance goal for all 

months of the year. 

3.5.5 Other PM2.5 (Soil) Model Performance 

Figure 3-5 displays the monthly variation in the Soil fractional bias using IMPROVE 

measurements in the CENRAP region. During the winter months, the model exhibits a very 

large (> 100%) overestimation bias. With the exception of July, the summer monthly bias is 

toward a slight overprediction but generally less than 20%. The July underestimation bias 

appears to be driven by impacts of high Soil values from wind blown dust events (e.g., see July 

2002 discussion in Appendix C). The Bugle Plot indicates that the summer Soil performance 

achieves the PM performance goal, a few months in the Spring/Fall period fall between the 

performance goal and criteria and the winter Soil performance exceeds the model performance 

criteria. Thus, the Soil performance is a cause for concern. 

3.5.6 Coarse Mass (CM) Model Performance 

The monthly average fractional bias values for CM are shown in Figure 3-6. In the winter the 

underprediction bias is typically in the -60% to -80% range. In the late Spring and Summer the 

underprediction bias ranges from -120% to -160%. As this underprediction bias is nearly 

systematic (i.e., an underprediction almost always occurs), then the fractional errors are the same 

magnitude as the bias. 

The Bugle Plots clearly show that the CM model performance is a problem. The monthly bias 

exceeds both the performance goal and criteria for almost every month of the year. 
 

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Figure 3-4. Monthly fractional bias (%) for elemental carbon (EC) across the CENRAP region 

for the CMAQ 2002 36 km Base F base case simulation. 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-11 

Figure 3-5. Monthly fractional bias (%) for other PM2.5 (Soil) across the CENRAP region for the 

CMAQ 2002 36 km Base F base case simulation. 
 

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Figure 3-6. Monthly fractional bias (%) for coarse mass (CM) across the CENRAP region for 

the CMAQ 2002 36 km Base F base case simulation. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-13 

3.6 Diagnostic Model Performance Evaluation 

The CASTNet and AQS networks also measure gas-phase species that are PM precursor or 

related species. The diagnostic evaluation of the 2002 36 km Base F CMAQ base case 

simulation for these compounds and the four seasonal months are presented in Appendix C. The 

displays for January are provided below as an example; the reader is referred to Appendix C for 

the rest of the monthly displays. 

The CASTNet network measures weekly average samples of SO2, SO4, NO2, HNO3, NO3 and 

NH4. The AQS network collects hourly measurements of SO2, NO2, O3 and CO. A 

comparison of the SO2 and SO4 performance provides insight into whether the SO4 formation 

rate may be too slow or fast. For example, if SO4 is underestimated and SO2 is overestimated 

that may indicate chemical conversion rates that are too slow. Analyzing the performance for 

SO4, HNO3, NO3, Total NO3 and NH4 provides insight into the equilibrium of these species. 

For example, if Total NO3 performs well but HNO3 and NO3 do not, then there may be issues 

associated with the partitioning between the gaseous and particulate phases of nitrate. Causes for 

incorrect HNO3/NO3 partitioning could include inadequate ammonia emissions and/or poorly 

characterized meteorological conditions (e.g., temperature). 

3.6.1 Diagnostic Model Performance in January 2002 

In January, SO2 is overstated across both the CASTNet and AQS sites with fractional bias values 

of 38% (Figure 3-7) and 31% (Figure 3-8), respectively. SO4 is understated by -34% across the 

CASTNet monitors (Figure 3-7) and -12% and -13% for the IMPROVE and STN networks 

(Figure C-4a). Wet SO4 deposition is also overstated in January (+40%, Figure C-4a). Given 

that SO2 emissions are well characterized, these results suggest that the January SO4 

underestimation may be partly due to understated transformation rates of SO2 to SO4 and 

overstated wet SO4 deposition. 

Total NO3 is overestimated by 35% on average across the CASTNet sites in the CENRAP 

region in January (Figure 3-7). HNO3 is underestimated (-34%) and particle NO3 is 

overestimated (+61%) suggesting there are gas/particle equilibrium issues. An analysis of the 

time series of the four CASTNet stations reveals that NO3, HNO3 and NH4 performance is 

actually very reasonable at the west Texas site and the HNO3 underestimation and NO3 

overestimation bias is coming from the east Kansas, central Arkansas and northern Minnesota 

CASTNet sites (see Figure C-3 for site locations). One potential contributor for this 

performance problem could be overstated NH3 emissions. However, the Total NO3 

overestimation bias suggests that the model estimated NOx oxidation rate may be too high in 

January. 

The SO2, NO2, O3 and CO performance across the AQS sites in January is shown in Figure 3-8. 

The AQS monitoring network is primarily an urban-oriented network. So, it is not surprising 

that the model is underestimating concentrations of primary emissions when a 36 km grid is 

used. NO2 is underestimated by approximately 5%, and CO by approximately 67%. Ozone is 

also underestimated on average, especially the maximum values above 60 ppb. 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-14 

Figure 3-7. January 2002 performance at CENRAP CASTNet sites for SO2 (top left), SO4 (top 

right), HNO3 (middle left), NO3 (middle right), Total NO3 (bottom left) and NH4 (bottom right). 
 

September 2007 

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Figure 3-8. January 2002 performance at CENRAP AQS sites for SO2 (top left), NO2 (top right), 

O3 (bottom left) and CO (bottom right).
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-16 

3.6.2 Diagnostic Model Performance In April 

In April there is an average SO2 overestimation bias across the CASTNet (+15%) and 

underestimation bias across the AQS (-10%) networks (Figures C-42 and C-43). SO4 is 

underestimated across all networks by -30% to -58% (Figure C-5a). The wet SO4 deposition 

bias is near zero. Both SO2 and SO4 are underestimated at the west Texas CASTNet monitor in 

April suggesting SO2 emissions in Mexico are likely understated. 

The HNO3 performance in April is interesting with almost perfect agreement except for 5 

modeled-observed comparisons that drives the average underprediction bias of -29% (Figure C42). On Julian Day 102 there is high HNO3 at the MN, KS and OK CASTNet sites that is not 

captured by the model. Given that HNO3, NO3 and Total NO3 are all underestimated by about 

the same amount (-30%), then part of the underestimation bias is likely due to too slow oxidation 

of NOx. 

There is a lot of scatter in the NO2 and O3 performance that is more or less centered on the 1:1 

line of perfect agreement with bias values of -8% and -21%, respectively (Figure C-43). CO is 

underestimated by -72% with the model unable to predict CO concentrations above 1 ppm due to 

the use of the coarse 36 km grid spacing. Mobile sources produce a vast majority of the CO 

emissions. So, AQS monitors for CO compliance are located near roadways, which are not 

simulated well using a 36 km grid. 

3.6.3 Diagnostic Model Performance In July 

In July SO2 is slightly underestimated across the CASTNet (-5%) and AQS (-12%) networks 

(Figures C-44 and C-45). SO4 is more significantly underestimated across all networks 

(-22% to -53%, as shown in Figure C-6a). Since wet deposition SO4 is also underestimated, it is 

unclear why all sulfur species are underestimated. 

The nitrate species are also all underestimated with the Total NO3 bias (-56%) being between the 

HNO3 bias (-35%) and NO3 bias (-115%). The modeled NO3 values are all near zero with little 

correlation with the observations, whereas the observed HNO3 and Total NO3 is tracked well 

with correlation coefficients of 0.74 and 0.76. These results suggest that the July NO3 model 

performance problem is partly due to insufficient formation of Total NO3, but mainly due to 

incorrect partitioning of the Total NO3. 

Again, there is abundant scatter in the AQS NO2 scatter plot for July (Figure C-45) resulting in a 

low bias (0%) but high error (65%). Ozone performance also exhibits a low bias (-15%) and 

error (20%), but the model is incapable of simulating ozone above 100 ppb. Although CO 

performance in July is better than the previous months, it still has a large underestimation bias of 

82%. 
 

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3.6.4 Diagnostic Model Performance In October 

SO2 is overstated in October across the CASTNet (+28%) and AQS (+33%) sites (Figures C-46 

and C-47). Although SO4 is understated across the CASTNet sites (-24%), the bias across the 

IMPROVE (-6%) and STN (0%) sites are near zero (Figure C-7a). 

Performance for HNO3 is fairly good with a low bias (+12%) and error (30%). But NO3 is 

overstated ( +34%) leading to an overstatement of Total NO3 (+37%). The overstatement of 

NO3 leads to an overstatement of NH4 as well (Figure C-46) 

As seen in the other months, NO2 exhibits a lot of scatter resulting in a low correlation (0.22) 

and high error (61%) but low bias (12%). The model tends to underpredict the high and 

overpredict the low O3 observations resulting in a -29% bias and low correlation coefficient. CO 

is also underpredicted (-76%) for the reasons discussed previously. 

3.7 Performance at CENRAP Class I Areas for the Worst and Best 20 Percent Days 

In this section, and in section C.5 of Appendix C, we present the results of the model 

performance evaluation at each of the CENRAP Class I areas for the worst and best 20 percent 

days. Performance on these days is critical since they are the days used in the 2018 visibility 

projections discussed in Chapter 4. For each Class I area we compared the predicted and 

observed extinction of the worst and best 20 percent days below. In Appendix C the PM speciesspecific extinction is also compared for the worst 20 percent days. 

3.7.1 Caney Creek (CACR) Arkansas 

The ability of the CMAQ model to estimate visibility extinction at the CACR Class I area on the 

2002 worst and best 20 percent days is provide in Figures 3-9 and C-48. On most of the worst 

20 percent days at CACR total extinction is dominated by SO4 extinction with some extinction 

due to OMC. On four of the worst 20 percent days extinction is dominated by NO3. The 

average extinction across the worst 20 percent days is underestimated by -33% (Figure 3-9), 

which is primarily due to a -51% underestimation of SO4 extinction combined with a 6% 

overestimation of NO3 extinction (Figure C-48). Performance for OMC extinction at CACR on 

the worst 20 percent days is pretty good with a -20% bias and 36% error. EC extinction is 

systematically underestimated. Soil extinction has low bias (-19%) buts lots of scatter and high 

error (74%), while CM extinction is greatly underestimated (bias of -153%). 

On the best 20 percent days at CACR the observed extinction ranges from 20 to 40 Mm-1. 

Whereas, the modeled extinction has a much larger range from 15 to 120 Mm-1. Much of the 

modeled overestimation of total extinction on the best 20% days (+44% bias) is due to NO3 

overestimation (+94% bias). 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-18 

Worst 20% Obs (left) vs Typ02g (right) at CACR1

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80 128 143 155 170 173 188 191 200 203 215 218 221 239 242 248 251 254 257 260 296 320 341 _ _ Avg

Julian Day in Worst 20% group

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Best 20% Obs (left) vs Typ02g (right) at CACR1

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11 14 26 89 98 116 197 266 278 284 287 293 299 302 311 323 329 335 338 353 356 359 _ _ Av

Julian Day in Best 20% group

bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 3-9. Daily extinction model performance at Caney Creek (CACR), Arkansas for the 

worst (top) and best (bottom) 20 percent days during 2002.
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-19 

3.7.2 Upper Buffalo (UPBU) Arkansas 

Model performance at the UPBU Class I area for the worst and best 20 percent days is shown in 

Figures 3-10 and C-49. On most of the worst 20 percent days at UPBU, visibility impairment is 

dominated by SO4, although there are also two high NO3 days. The model underestimates the 

average of the total extinction on the worst 20 percent days at UPBU by -40% (Figure 3-10), 

which is due to an underestimation of extinction due to SO4, OMC and CM by -46%, -33% and -

179%, respectively. 

On the best 20 percent days at UPBU, the model performs reasonably well with a low bias (2%) 

and error (42%). But again, the model has a much wider range in extinction values across the 

best 20 percent days (15 to 120 Mm-1) than observed (20 to 45 Mm-1). There are five days in 

which the modeled NO3 overprediction is quite severe and when those days are removed the 

range in the modeled and observed extinction on the best 20 percent days is quite similar to the 

observed, although the model gets much cleaner on the very cleanest modeled days. 

Worst 20% Obs (left) vs Typ02g (right) at UPBU1

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128 143 158 170 173 179 191 197 203 206 215 218 221 224 239 242 248 251 254 257 269 296 320 332 _ Avg

Julian Day in Worst 20% group

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Best 20% Obs (left) vs Typ02g (right) at UPBU1

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14 26 47 68 89 98 116 119 164 167 176 227 266 278 281 287 290 302 311 323 335 353 _ _ Av

Julian Day in Best 20% group

bEXT (1/Mm)

bCM

bSOIL

bEC

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bNO3

bSO4

Figure 3-10. Daily extinction model performance at Upper Buffalo (UPBU), Arkansas for the 

worst (top) and best (bottom) 20 percent days during 2002.
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-20 

3.7.3 Breton Island (BRET), Louisiana 

The observed total extinction on the worst 20 percent days at Breton Island is underestimated by 

-71% (Figure 3-11), which is due to an underestimation of each component of extinction (Figure 

C-50) by from -50% to -70% (SO4, OMC and Soil) to over -100% (EC and CM). The observed 

extinction on the worst 20 percent days ranges from 90 to 170 Mm-1, whereas the modeled 

values drop down to as low as approximately 15 Mm-1. On the best 20 percent days the range 

of the observed and modeled extinction is similar (roughly 10 to 50 Mm-1) that results in a 

reasonably low bias (-22%), but there is little agreement on which days are higher or lower 

resulting in a lot of scatter and high error (54%). 

Worst 20% Obs (left) vs Typ02g (right) at BRET1

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44 47 74 125 143 179 194 197 200 206 215 239 248 251 254 257 260 263 272 281 284 287 305 311 _ Avg

Julian Day in Worst 20% group

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Best 20% Obs (left) vs Typ02g (right) at BRET1

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8 14 32 38 68 101 119 122 158 170 176 185 188 191 209 212 233 236 245 299 302 335 356 _ Av

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bEXT (1/Mm)

bCM

bSOIL

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bNO3

bSO4

Figure 3-11. Daily extinction model performance at Breton Island (BRET), Louisiana for the worst 

(top) and best (bottom) 20 percent days during 2002.
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-21 

3.7.4 Boundary Waters (BOWA), Minnesota 

There are three types of days during the worst 20 percent days at BOWA: SO4 days, OMC days 

and NO3 days (Figure 3-12). The two high OMC days are likely fire impact events that the 

model captures to some extent on one day and not on the other. On the five high (> 20 Mm-1) 

NO3 extinction days the model predicts the observed extinction well on three days and 

overestimates by a factor of 3-4 on the other two high NO3 days. SO4 is underestimated by -

43% on average across the worst 20 percent days at BOWA. 

With the exception of two days, the model reproduces the total extinction for the best 20 percent 

days at BOWA quite well with a bias and error value of +14% and 22% (Figure 3-12). Without 

these two days, the modeled and observed extinction both range between 15 and 25 Mm-1. 

Worst 20% Obs (left) vs Typ02g (right) at BOWA1

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11 26 146 149 161 170 176 179 188 197 200 203 242 245 251 275 299 314 332 344 347 _ _ _ _ Avg

Julian Day in Worst 20% group

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Best 20% Obs (left) vs Typ02g (right) at BOWA1

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29 53 62 83 110 116 122 125 134 137 140 185 218 230 239 266 329 350 356 359 _ _ _ _ Av

Julian Day in Best 20% group

bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 3-12. Daily extinction model performance at Boundary Waters (BOWA), Minnesota for the 

worst (top) and best (bottom) 20 percent days during 2002.
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-22 

3.7.5 Voyageurs (VOYA) Minnesota 

VOYA is also characterized by SO4, NO3 and OMC days (Figure 3-13). Julian Days 179 and 

200 are high OMC days that were also high OMC days at BOWA again indicating impacts from 

fires in the area that is not fully captured by the model. SO4 and NO3 performance is fairly good 

and, without the fire days, OMC performance looks good as well (Figure C-52). On the best 20 

percent days there is one day the modeled extinction is much higher than observed and a few 

others that are somewhat higher, but for most of the best 20 percent days the modeled extinction 

is comparable to the observed values. 

Worst 20% Obs (left) vs Typ02g (right) at VOYA2

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5 11 71 86 104 131 146 152 170 179 188 197 200 221 242 245 254 260 272 284 299 332 344 347 _ Avg

Julian Day in Worst 20% group

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Best 20% Obs (left) vs Typ02g (right) at VOYA2

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29 47 50 56 116 122 125 128 134 137 140 185 203 218 239 266 275 290 296 329 350 356 _ _ Av

Julian Day in Best 20% group

bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 3-13. Daily extinction model performance at Voyageurs (VOYA), Minnesota for the 

worst (top) and best (bottom) 20 percent days during 2002.
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-23 

3.7.6 Hercules Glade (HEGL) Missouri 

On most of the worst 20 percent days at HEGL the observed extinction ranges from 120 to 220 

Mm-1 whereas model extinction ranges from 50 to 170 Mm-1 (Figure 3-14). However, there is 

one extreme day with extinction approaching 400 Mm-1 that the model does a very good job in 

replicating. Over all the days there is a modest underestimation bias in SO4 (-39%) and OMC 

(-39%) extinction, larger underestimation bias in EC (-62%) and CM (-118%) extinction and 

overestimation bias in Soil (+30%) extinction (Figure C-53). 

On the best 20 percent days there is one day where the model overstates the observed extinction 

by approximately a factor of four and a handful of other days that the model overstates the 

extinction by a factor of 2 or so, but most of the days both the model and observed extinction 

sites are around 40 Mm-1 ±10 Mm-1. On the best 20 percent days, when the observed extinction 

is overstated, it is due to overstatement of the NO3. 

Worst 20% Obs (left) vs Typ02g (right) at HEGL1

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5 149 152 170 173 179 188 191 206 215 218 221 224 239 242 245 248 251 257 260 269 296 320 341 347 Avg

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Best 20% Obs (left) vs Typ02g (right) at HEGL1

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14 26 44 68 71 89 113 116 119 134 137 227 266 278 281 287 290 302 308 311 317 335 353 _ Av

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bSO4

Figure 3-14. Daily extinction model performance at Hercules Glade (HEGL), Missouri for the 

worst (top) and best (bottom) 20 percent days during 2002.
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-24 

3.7.7 Mingo (MING) Missouri 

The worst 20 percent days at MING are mainly high SO4 days with a few high NO3 days that 

the model reproduces reasonably well resulting in low bias (+10%) and error (38%) for total 

extinction (Figure 3-15). The PM species specific performance is fairly good with low bias for 

SO4 (+4%), good agreement with NO3 on high NO3 days except for one day, low OMC (+23%) 

and EC (+3%) bias and larger bias in EC (+37%) and CM (-105%) extinction (Figure C-54). 

For the best 20 percent days, there is one day the model is way too high due to overstated NO3 

extinction and a few other days the model overstates the observed extinction that is usually due 

to overpredicted NO3, but on most of the best 20 percent days the modeled extinction is 

comparable to the observed values. This results in low bias (+12%) and error (36%) for total 

extinction at MING for the best 20 percent days. 

Worst 20% Obs (left) vs Typ02g (right) at MING1

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20 77 95 158 173 185 188 191 194 197 206 215 218 221 239 242 245 251 272 296 332 341 _ _ _ Avg

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Best 20% Obs (left) vs Typ02g (right) at MING1

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14 32 44 62 116 134 137 140 164 167 176 200 227 263 266 278 281 287 290 308 353 356 _ _ Av

Julian Day in Best 20% group

bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 3-15. Daily extinction model performance at Mingo (MING), Missouri for the worst (top) 

and best (bottom) 20 percent days during 2002.
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-25 

3.7.8 Wichita Mountains (WIMO), Oklahoma 

With the exception of an overprediction on day 344 due to NO3, observed total extinction on the 

worst 20 percent days at WIMO is understated with a bias of -42% (Figure 3-16) that is primarily 

due to an underestimation of extinction due to SO4 (-48%) and OMC (-69%) (Figure C-55). 

CMAQ total extinction performance for the average of the best 20 percent days at WIMO is 

characterized by an overestimation bias (+21%) on most days that is primarily due to NO3 

overprediction on several days. Again the modeled range of extinction on the best 20 percent 

days (12-60 Mm-1) is much greater than observed (20-35 Mm-1). 

Worst 20% Obs (left) vs Typ02g (right) at WIMO1

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29 83 95 101 110 113 125 128 131 143 170 173 176 179 218 221 224 242 245 254 260 269 272 344 _ Avg

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Best 20% Obs (left) vs Typ02g (right) at WIMO1

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8 11 14 26 32 41 44 47 53 98 116 263 278 281 287 302 311 314 317 323 353 356 _ _ Av

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bNO3

bSO4

Figure 3-16. Daily extinction model performance at Wichita Mountains (WIMO), Oklahoma for 

the worst (top) and best (bottom) 20 percent days during 2002.
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-26 

3.7.9 Big Bend (BIBE) Texas 

The observed extinction on the worst 20 percent days at BIBE is underpredicted on almost every 

day resulting in a fractional bias value of -72% (Figure 3-17). Every component of extinction is 

underestimated on average for the worst 20 percent days (Figure C-56) with the underestimation 

bias ranging from -24% (OMC) to -162% (CM). SO4 extinction, that typically represents the 

largest component of the total extinction is understated by -94%. 

The model does a better job in predicting the total extinction at BIBE for the best 20 percent days 

with average fractional bias and error values of +13% and 19% (Figure 3-17). With the 

exception of one day that the observed extinction is overestimated by approximately a factor of 

2, the modeled and observed extinction on the best 20 percent days at BIBE are both within 12 to 

25 Mm-1. However, there are some mismatches with the components of extinction with the 

model estimating much lower contributions due to Soil and CM. 

Worst 20% Obs (left) vs Typ02g (right) at BIBE1

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29 71 89 92 95 101 110 113 116 119 125 128 131 143 167 191 218 221 230 242 245 254 275 329 _ Avg

Julian Day in Worst 20% group

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Best 20% Obs (left) vs Typ02g (right) at BIBE1

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11 14 38 56 77 194 212 263 281 287 299 302 308 320 326 338 344 347 350 353 359 362 _ _ Av

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bSO4

Figure 3-17. Daily extinction model performance at Big Bend (BIBE), Texas for the worst (top) 

and best (bottom) 20 percent days during 2002.
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-27 

3.7.10 Guadalupe Mountains (GUMO) Texas 

Most of the worst 20 percent days at GUMO are high dust days with high Soil and CM that is not 

captured by the model (Figure 3-18). Extinction due to Soil and CM on the worst 20 percent 

days is underestimated by -105% and -191%, respectively (Figure C-57). Better performance is 

seen on the best 20 percent days with bias and error for total extinction of 8% and 21%, but the 

model still understates Soil and CM. 

Worst 20% Obs (left) vs Typ02g (right) at GUMO1

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74 83 92 95 122 131 137 146 161 164 167 170 173 176 191 218 221 245 251 254 260 266 272 329 _ Avg

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Best 20% Obs (left) vs Typ02g (right) at GUMO1

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bSO4

Figure 3-18. Daily extinction model performance at Guadalupe Mountains (GUMO), Texas for 

the worst (top) and best (bottom) 20 percent days during 2002.
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_3_MPE3.doc 3-28 

3.8 Model Performance Evaluation Conclusions 

The model performance evaluation reveals that the model is performing best for SO4, OMC and 

EC. Soil performance is mixed with a winter overestimation bias with lower bias and higher 

error in the summer. CM performance is poor year round. The operational evaluation reveals 

that SO4 performance usually achieves the PM model performance goal and always achieves the 

model performance criteria, although it does have an underestimation bias that is greatest in the 

summer. NO3 performance is characterized by a winter overestimation bias with an even greater 

summer underestimation bias. However, the summer underestimation bias occurs when NO3 is 

very low and when it is not an important component of the observed or predicted PM mass 

concentrations or component of visibility impairment. Performance for OMC meets the model 

performance goal year round at the IMPROVE sites, but is characterized by an underestimation 

bias at the more urban STN sites. EC exhibits very low bias at the STN sites and a summer 

underestimation bias at the IMPROVE sites, but meets the model performance goal throughout 

the year. Soil has a winter overestimation bias that is outside of the model performance goal and 

criteria raising questions whether the model should be used for this species. Finally, CM 

performance is extremely poor with an underprediction bias that is outside of the performance 

goal and criteria. We suspect that much of the CM concentrations measured at the IMPROVE 

sites is due to highly localized emissions from fugitive dust sources that are not included in the 

emissions inventory and would be difficult to simulate using 36 km regional modeling. 

Performance for the worst 20 percent days at the CENRAP Class I areas is generally 

characterized by an underestimation bias. Performance at the BRET, BIBE and GUMO Class I 

areas for the worst 20 percent days is particularly suspect and care should be taken in the 

interpretation of the visibility projections at these three Class I areas. 

The CMAQ 2002 36 km model appears to be working well enough to reliably make future-year 

projections for changes in SO4, NO3, EC and OMC at the rural Class I areas. Performance for 

Soil and especially CM is suspect enough that care should be taken in interpreting these 

modeling results. The model evaluation focused on the model’s ability to predict the 

components of light extinction mainly at the Class I areas. Additional analysis would have to be 

undertaken to examine the model’s ability to simulate ozone and fine particulate to address 8-

hour ozone and PM2.5 attainment issues. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-1 

4.0 VISIBILITY PROJECTIONS 

This section presents the future-year visibility projections for Class I areas within and near the 

CENRAP states and their comparison with the 2018 Uniform Rate of Progress (URP) point. As 

noted in Chapter 1, the Regional Haze Rule (RHR) requires states with Class I areas to develop 

State Implementation Plans (SIPs) that include reasonable progress goals (RPGs) for improving 

visibility in each Class I area and emission reduction measures to meet those goals. For the 

initial SIPs due in December 2007, states are required to adopt RPGs for improving visibility 

from Baseline Conditions. The 2000-2004 five-year period is used to define Baseline Conditions 

and the first future progress period is 2018. A state is required to set RPGs for each Class I area 

in the state for two visibility metrics: 

• Provide for an improvement in visibility for the most impaired visibility days (i.e., the 

worst 20 percent days); and 

• Ensure no degradation in visibility for the least impaired visibility days (i.e., the best 20 

percent days). 

The goal of the RPGs is to provide for a rate of improvement sufficient to be on a course to attain 

“Natural Conditions” by 2064. States are to define controls to meet RPGs every 10 years, 

starting in 2018, which defines progress periods ending in 2018, 2028, 2038, 2048, 2058 and 

finally 2064. States will determine whether they are meeting their goals by comparing visibility 

conditions from one five-year period to another (e.g., 2000-2004 to 2013-2017). As stated in 40 

CFR 51.308 (d) (1), baseline visibility conditions, reasonable progress goals, and changes in 

visibility must be expressed in terms of deciview (dv) units. The haze index (HI) metric of 

visibility impairment, in deciviews, is derived from light extinction (bext) as follows: 

HI = 10 ln (bext/10), 

Where light extinction (bext) is expressed in terms of inverse megameters (Mm-1 = 10-6 m

-1). 

Light extinction (bext) is calculated using the observed fine particulate concentrations from the 

IMPROVE monitors using either the original or the new IMPROVE aerosol extinction equation. 

Both equations are discussed below. 

4.1 Guidance for Visibility Projections 

EPA has published several guidance documents that relate to how modeling results should be 

used to project future-year visibility and how states should define RPGs: 

“Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of 

Air Quality Goals for Ozone, PM2.5 and Regional Haze” (EPA, 2007a). 

“Guidance for Tracking Progress Under the Regional Haze Rule” (EPA, 2003a). 

“Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule” 

(EPA, 2003b). 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-2 

“Guidance for Setting Reasonable Progress Goals Under the Regional Haze Program” 

(EPA, 2007b). 

The first EPA modeling guidance document listed above (EPA, 2007) discusses the use of 

modeling results to project future-year visibility. The second EPA guidance document (EPA, 

2003a) focuses on monitored visibility, how to define the visibility Baseline Conditions and how 

to track visibility goals. The third EPA guidance document discusses procedures for defining 

Natural Conditions for a Class I area. Natural Conditions are the visibility goal for 2064. 

Although states may propose alternative approaches for defining Natural Conditions, in this 

section we use the default Natural Conditions at Class I areas (EPA, 2003b; Pitchford, 2006). 

The final EPA guidance document discusses how states should define their RPGs and their 

relationship to the 2018 URP point. 

The EPA documents discussed above are followed for the visibility projections presented in this 

section with one notable exception. Some of the EPA documents are based on the original 

IMPROVE equation (e.g., EPA, 2003a, b). The CENRAP visibility projections are based on the 

new IMPROVE equation, although projections based on the original IMPROVE equation are 

also presented as an alternative approach in Chapter 5. EPA guidance allows for using either the 

original or the new IMPROVE equation (EPA, 2007a; Timin, 2007). CENRAP, along with the 

other RPOs, have elected to use the new IMPROVE equation for their visibility projections. 

4.2 Calculation of Visibility and 2018 URP Point from IMPROVE Measurements 

EPA guidance recommends using the model in a relative sense to project future-year visibility 

conditions (EPA, 2007a). This projection is made using Relative Response Factors (RRFs) that 

are defined as the ratio of the future-year modeling results to the base-year modeling results. 

The RRFs are applied to the baseline visibility conditions to project future-year visibility. The 

major features of EPA’s recommended visibility projection approach are as follows (EPA, 

2003a,b; 2007a): 

• Monitored data are used to define current visibility Baseline Conditions using 

IMPROVE monitoring data from the 2000-2004 five-year base period. 

• Monitored concentrations of PM10 are divided into six major components, the first 

five of which are assumed to be PM2.5 and the sixth is coarse mass (CM or PM2.5-10). 

3⁄4 SO4 (sulfate) that is assumed to be ammonium sulfate [(NH4)2SO4]; 

3⁄4 NO3 (particulate nitrate) that is assumed to be ammonium nitrate [NH4NO3]; 

3⁄4 OC (organic carbon) that is assumed to be total organic mass carbon (OMC) 

3⁄4 EC (elemental carbon); 

3⁄4 IP (other fine inorganic particulate or Soil); and 

3⁄4 CM (coarse mass). 

• Models are used in a relative sense to develop RRFs between baseline and future 

predicted concentrations of each component. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-3 

• PM component-specific RRFs are multiplied by observed Baseline monitored values 

to estimate future-year PM component concentrations. 

• Estimates of future-year component concentrations are consolidated to provide an 

estimate of future-year air quality and visibility using either the original or new 

IMPROVE equation. 

• Future-year model projected visibility is compared with the 2018 point on the URP 

glidepath to assist in evaluating the visibility improvements. 

• It is assumed that all measured sulfate is in the form of ammonium sulfate 

[(NH4)2SO4] and all particulate nitrate is in the form of ammonium nitrate [NH4NO3]. 

In order to facilitate tracking visibility progress, three important visibility concepts are required 

for each Class I area: 

Baseline Conditions: Baseline Conditions represent visibility for the 20 percent best (B20%) 

and 20 percent worst (W20%) visibility days for the initial five-year baseline period of the 

regional haze program. Baseline Conditions are calculated using IMPROVE monitor data 

collected during the 2000-2004 five-year period and are the starting point in 2004 for the 

URP glidepath and 2018 visibility projections. 

Natural Conditions: Estimates of natural visibility conditions for the best 20 percent and 

worst 20 percent days at a Class I area (i.e., visibility conditions that would be experienced in 

the absence of human-caused impairment). EPA has defined a set of default Natural 

Conditions for the original IMPROVE equation (EPA, 2003b) that has been updated to the 

new IMPROVE equation by the Natural Haze Levels II Committee (Pitchford, 2006) that we 

have used in this Chapter. 

2018 URP Point: The 2018 Uniform Rate of Progress (URP) point is defined by defining a 

linear glidepath in deciviews starting with the 2000-2004 Baseline Conditions in 2004 and 

ending at Natural Conditions in 2064. Where the linear glidepath passes through 2018 is the 

2018 URP point in deciviews. 

4.2.1 Calculation of Visibility from IMPROVE PM Measurements 

Baseline Conditions for Class I areas are calculated using the procedures in EPA’s guidance 

document (EPA, 2003a) and fine and coarse particulate matter concentrations measured at 

IMPROVE monitors (Malm et al, 2000; Debell et al., 2006). Currently, each Class I area in the 

CENRAP domain has an associated IMPROVE monitor. The IMPROVE monitors do not 

directly measure visibility, but instead measure speciated fine particulate (PM2.5) and total PM2.5

and PM10 mass concentrations from which visibility is obtained through the IMPROVE equation. 

Visibility conditions are estimated starting with the IMPROVE 24-hour average mass 

measurements for six PM species: 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-4 

• Sulfate [(NH4)2SO4]; 

• Particulate Nitrate [(NH4NO3]; 

• Organic Matter Carbon or Organic Mass by Carbon [OMC]; 

• Elemental Carbon [EC] or Light Absorbing Carbon [LAC]; 

• Other fine particulate [Soil]; and 

• Coarse Matter or Coarse Mass [CM]. 

The IMPROVE monitors do not directly measure some of these species so assumptions are made 

as to how the IMPROVE measurements can be adjusted and combined to obtain these six 

components of light extinction. For example, in the IMPROVE equation sulfate and particulate 

nitrate are assumed to be completely neutralized by ammonium. In addition, only the fine mode 

(PM2.5) of PM is speciated by the IMPROVE monitor to obtain sulfate and nitrate measurements 

(that is, any coarse mode sulfate and nitrate in the real atmosphere may be present in the CM 

IMPROVE measurement). Concentrations for the above six components of light extinction in 

the IMPROVE equation are obtained from the IMPROVE measured species using the mappings 

shown in Table 4-1: 

Table 4-1. Definition of IMPROVE PM Components from Measured IMPROVE Species. 

IMPROVE Component IMPROVE Measured Species 

Sulfate 1.375 x (3 x S) 

Nitrate 1.29 x NO3 

-

OMC 1.4*OC (original IMPROVE) and 1.8*OC (new IMPROVE) 

LAC EC 

Soil 2.2*AL + 2.49*SI + 1.63*CA + 2.42*FE + 1.94*TI 

CM MT – MF 

Where: 

• S is elemental sulfur as determined from proton induced x-ray emissions (PIXE) analysis 

of the IMPROVE Module A1

. To estimate the mass of the sulfate ion (SO4

=

), S is 

multiplied by 3 to account the presence of oxygen. If S is missing then the sulfate (SO4) 

measured by ion chromatography analysis of the Module B is used to replace (3 x S). For 

the IMPROVE aerosol extinction calculation, Sulfate is assumed to be completely 

neutralized by ammonium (1.375 x SO4). 

• NO3

-

 is the particulate nitrate measured by ion chromatography analysis of the Module B. 

For the IMPROVE aerosol extinction calculation, it is assumed to be completely 

neutralized by ammonium (1.29 x NO3

-

). 

• The IMPROVE Organic Carbon (OC) measurements are multiplied by 1.4 to obtain 

Organic Mass Carbon (OMC) using the original IMPROVE equation and multiplied by 

1.8 for the new IMPROVE equation. This adjustment of the measured OC accounts for 

mass due to other elements in the OMC besides Carbon. 

• Elemental Carbon (EC) is also referred to as Light Absorbing Carbon (LAC). 

 

1

 The IMPROVE sampler consists of four independent modules (A, B, C and D). Each module incorporates a 

separate inlet, filter pack and pump assembly and are controlled by a common timing mechanism. Module A 

measures fine PM mass and elements. Module B measures sulfate and nitrate ions. Module C measures EC and 

OC. Module D measures PM10 mass. (see http://vista.cira.colostate.edu/improve/ for more details). 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-5 

• Soil is determined as a sum of the masses of those elements (measured by PIXE) 

predominantly associated with soil (Al, Si, Ca, Fe, K and Ti), adjusted to account for 

oxygen associated with the common oxide forms. Since K and FE are products of the 

combustion of vegetation, they are both represented in the formula by 0.6 x Fe and K is 

not shown explicitly. 

• MT and MF are total PM10 and PM2.5 mass, respectively. 

4.2.1.1 Original and New IMPROVE Equations 

Associated with each PM species is an extinction efficiency that converts concentrations (in 

μg/m3

) to light extinction (in inverse megameters, Mm-1). Sulfate and nitrate are hygroscopic 

which means that they can absorb water from the atmosphere which changes their extinction 

efficiency. This is accounted for through relative humidity adjustment factors [f(RH)] that 

increase the particle’s extinction efficiency with increasing RH to account for the particles taking 

on water Note that some OMC may also have hygroscopic properties, but the IMPROVE 

equations assume OMC is non-hygroscopic. 

There are currently two IMPROVE equations that are used to convert the measured PM 

concentrations to light extinction, the original (or old) and the new IMPROVE equations. 

4.2.1.1.1 Original IMPROVE Equation 

The original IMPROVE equation that converts PM species concentrations to light extinction is 

given as follows: 

bSulfate = 3 x f(RH) x [Sulfate] 

bNitrate = 3 x f(RH) x [Nitrate] 

bEC = 10 x [EC] 

bOMC = 4 x [OMC] 

bSoil = 1 x [Soil] 

bCM = 0.6 x [CM] 

Monthly average f(RH) factors are used as recommended in EPA’s guidance (EPA, 2003a). 

These values are available in the final EPA guidance document (EPA, 2003a) and at: 

ftp://ftp.saic.com/raleigh/RegionalHaze_2002FRHcurve/fRH_analysis/. 

The total light extinction (bext) is assumed to be the sum of the light extinction due to the six PM 

species listed above plus Rayleigh (blue sky) background (bRay) that is assumed to be 10 Mm-1. 

 bext = bRay + bSulfate + bNitrate + bEC +bOMC + bSoil + bCM

The total light extinction (bext) in Mm-1 is related to visual range (VR) in km using the following 

relationship: 

 VR = 3912 / bext, 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-6 

for bext in Mm-1. 

The Regional Haze Rule requires that visibility be expressed in terms of a haze index (HI) in 

units of deciviews (dv), which is calculated as follows: 

 HI = 10 ln(bext/10) 

4.2.1.1.2 New IMPROVE Equation 

The new IMPROVE equation is nonlinear in SO4, NO3 and OMC concentrations accounting for 

the different light scattering efficiency characteristics as a function of concentrations for these 

three species. It is expressed as follows: 

bSulfate = 2.2 x fS(RH) x [Small Sulfate] + 4.8 fS(RH) x [Large Sulfate] 

bNitrate = 2.4 x fS(RH) x [Small Nitrate] + 5.1 fS(RH) x [Large Nitrate] 

bEC = 10 x [Elemental Carbon] 

bOMC = 2.8 x [Small Organic Mass] + 6.1 x [Large Organic Mass] 

bSoil = 1 x [Fine Soil] 

bCM = 0.6 x [Coarse Mass] 

bNaCl = 1.7 x fSS(RH) x [Sea Salt] 

bNO2 = 0.33 x [NO2 (ppb)] 

The total Sulfate, Nitrate and OMC are each split into two fractions, representing small and large 

size distributions of those components. As noted in Table 4-1, the OMC is 1.8 times the 

IMPROVE OC measurement in the new IMPROVE algorithm, compared to 1.4 times the 

IMPROVE OC measurement in the original IMPROVE equation. New terms have been added 

for Sea Salt (important for coastal areas and possibly other areas)and for light absorption by NO2 

(only used where NO2 observations are available). As none of the CENRAP Class I area 

IMPROVE sites measure NO2 concentrations, then this component of the new IMPROVE 

equations was not used. Site-specific Rayleigh scattering for each IMPROVE monitoring site is 

used in the new IMPROVE equation, as compared to a constant 10 Mm-1 value assumed in the 

original IMPROVE equation. 

The apportionment of the Small and Large components of Sulfate, Nitrate and Organic Mass is 

done as follows: 

[Large Sulfate] = [Total Sulfate] / 20 x [Total Sulfate], for [Total Sulfate] < 20 μg/m3

[Large Sulfate ] = [Total Sulfate], for [Total Sulfate] > 20 μg/m3

[Small Sulfate] = [Total Sulfate] – [Large Sulfate] 

The same equations are used to apportion Total Nitrate and Total OMC among their Large and 

Small components. 

The total extinction (bext) in the new IMPROVE equations is the sum of all the extinction 

components associated with each PM species. The new IMPROVE equation adds Sea Salt and 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-7 

NO2 as noted above. In addition, site-specific Rayleigh background is used with the new 

IMPROVE equation: 

bext = bRay + bSulfate + bNitrate + bEC +bOMC + bSoil + bCM + bNaCl + bNO2

The Haze Index (HI) and Visual Range (VR) are calculated from the total extinction from the 

new IMPROVE equation using the same formulas as given above for the original IMPROVE 

equation. 

4.2.1.1.3 Justification for Using the New IMPROVE Equation 

The new IMPROVE equation was developed using the latest scientific information on PM 

species extinction properties combined with fitting reconstructed light extinction based on 

IMPROVE measured PM and NO2 concentrations with actual co-located measured light 

extinction (e.g., nephelometer measurements). Figure 4-1 displays example comparisons of 24-

hour light extinction using the original and new IMPROVE equations compared against 24-hour 

nephelometer measurements of light extinction at the Great Smoky Mountains Class I area 

IMPROVE monitor. The original IMPROVE equation has a bias toward understating light 

extinction at the high end and overstating it at the low end, whereas the new IMPROVE equation 

does a better job in estimating light extinction from measured PM at all extinction levels. 

Because the new IMPROVE equation is based on more recent science and fits the observed light 

extinction values better, the CENRAP states have elected to perform their primary visibility 

projections using the new IMPROVE equation. Results using the original IMPROVE equation 

are presented in Section 5 as an alternative approach. 

Figure 4-1. Comparisons of observed light extinction with reconstructed light extinction using the 

new (left) and original (right) IMPROVE equations at the Great Smoky Mountains National Park. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-8 

4.2.2 Calculation of the Baseline Conditions 

The visibility Baseline Conditions for the worst 20 percent and best 20 percent days is calculated 

from the IMPROVE observations from the 2000-2004 period for each Class I area following 

EPA’s guidance (EPA, 2003a). The basic procedures for calculating the Baseline Conditions are 

as follows: 

1. Determine whether the observed IMPROVE data for each site and year satisfies EPA’s 

minimal data capture criteria (EPA, 2003a). If there are less than three years with valid 

data capture for the 2000-2004 Baseline then the Baseline Conditions can not be calculated 

and data filling is needed. 

2. For each year in the 2000-2004 period with sufficient valid data, rank the visibility in 

terms of extinction or deciview using either the original or new IMPROVE equation and 

monthly average f(RH) factors (EPA, 2003a). 

3. For the worst 20 percent days, extract the 20% most impaired visibility days for each year 

(similarly for best 20 percent days extract 20% cleanest days). With a complete yearly 

data capture of IMPROVE 1:3 day sampling frequency this would result in 24 worst 20 

percent and 24 best 20 percent days in a year. 

4. For each worst 20 percent (or best 20 percent) day in each year, calculate 24-hour average 

visibility extinction using the IMPROVE measurements and either the original and new 

IMPROVE equation, convert the daily extinction to daily deciview and then average 

across each year to get yearly average deciview extinction for the worst 20 percent (or best 

20 percent) days for each valid year from the 2000-2004 period. 

5. Average the annual average deciview worst 20 percent (or best 20 percent) days deciview 

across each valid year in the 2000-2004 period (minimum of 3 valid years required) to get 

the worst 20 percent (or best 20 percent) Baseline Conditions. 

4.2.3 Data Filling for Sites with Insufficient Valid Data to Calculate Baseline Conditions 

Three CENRAP Class I areas did not contain sufficient IMPROVE observations during the fiveyear 2000-2004 Baseline to have three valid years of data from which Baseline Conditions could 

be constructed: Breton Island (BRET), Louisiana; Boundary Waters (BOWA), Minnesota and 

Mingo (MING), Missouri. For these three Class I areas, data filling was used to obtain sufficient 

data so that at least three-years of valid data were available from which Baseline Conditions 

could be calculated. These data filled IMPROVE databases were prepared and made available 

on the VIEWS website. More information on the data filling procedures can be found at the 

VIEWS website: (http://vista.cira.colostate.edu/views/). 

4.2.4 Natural Conditions 

EPA has published default Natural Conditions for Annual Average and the worst 20 percent and 

best 20 percent days based on the original IMPROVE equation (EPA, 2003b). These default 

Natural Conditions have been updated to the new IMPROVE equation by the Natural Haze 

Levels II Committee (Pitchford, 2006). These default Natural Conditions are used as the anchor 

point for the glidepaths in 2064 and are provided in Appendix D for the CENRAP Class I areas. 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-9 

4.2.5 2018 URP Point 

The 2018 point on the Uniform Rate of Progress (URP) glidepath is constructed by generating a 

linear glidepath in deciviews from the Baseline Conditions in 2004 to Natural Conditions in 

2064. Where the linear glidepath crosses 2018 is the 2018 point on the URP glidepath or the 

2018 URP point. Figure 4-2 displays an example linear glidepath for the Caney Creek Class I 

area in Arkansas. There are three years of sufficient valid IMPROVE data during the 2000-2004 

Baseline (2002, 2003 and 2004) with values of 27.21, 26.52 and 25.34 dv resulting in worst 20 

percent Baseline Conditions of 26.36 dv that is placed as the starting point in 2004 for the 

glidepath. The ending point for the glidepath is 11.58 dv which is the default Natural Conditions 

for the worst 20 percent days (EPA, 2003b; Pitchford, 2006). The linear glidepath crosses 2018 

at 22.91 dv which becomes the 2018 URP point. 

Uniform Rate of Reasonable Progress Glide Path

Caney Creek Wilderness - 20% Data Days

26.36

25.38

22.91

20.45

17.99

15.52

13.06

11.58

0

5

10

15

20

25

30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation

Figure 4-2. Linear Glidepath for Caney Creek (CACR), Arkansas that linearly connects the 

26.36 dv Baseline Conditions in 2004 with the 11.58 dv Natural Conditions in 2064 resulting in a 

22.91 dv 2018 URP Point. 

4.3 EPA Default Approach to Visibility Projections 

For CENRAP’s model application for a single year (2002), EPA’s regional haze modeling 

guidance recommends developing Class I area-specific and PM species-specific RRFs based on 

the average concentrations for the worst 20 percent days from 2002 (EPA, 2007). Thus, this is 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-10 

the methodology used to project 2018 visibility estimates in this section. For example, if 

SO4(2002)i and SO4(2018)i are the model estimated sulfate concentrations for the 2002 worst 20 

percent days (i=1...N) at a given Class I area for the 2002 and 2018 emission scenarios then the 

RRF for sulfate and this Class I area is given by: 

 RRF(SO4)i = ∑SO4(2018)i / ∑SO4(2002)i

4.3.1 Mapping of Modeling Results to the IMPROVE Measurements 

As noted above, to project future-year visibility at Class I areas the modeling results are used in a 

relative sense to scale current observed visibility for the worst 20 percent and best 20 percent 

visibility days using RRFs that are the ratio of modeling results for the future-year to currentyear. This scaling is done separately for each of the six components of light extinction in the 

IMPROVE equations. The CMAQ modeled species do not necessarily exactly match up with 

the IMPROVE PM species, thus assumptions must be made to map the modeled species to the 

IMPROVE PM species for the purpose of projecting visibility improvements. For example, 

CMAQ explicitly simulates ammonium and sulfate may or may not be fully neutralized in the 

model by ammonium, whereas the IMPROVE equations assume sulfate is fully neutralized by 

ammonium. For the CMAQ Version 4.5 (September 15, 2005 release) model, the mapping of 

modeled species to IMPROVE equation PM species is listed in Table 4-2. 

Table 4-2. Mapping of CMAQ V4.5 modeled species concentrations to IMPROVE PM 

components. 

IMPROVE 

Component 

CMAQ V4.3 Species 

Sulfate 1.375 x (ASO4J + ASO4I) 

Nitrate 1.29 x (ANO3J + ANO3I) 

LAC AECJ + AECI 

OMC AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI 

Soil A25J + A25I 

CM ACORS + ASEAS + ASOIL 

For the CENRAP visibility projections using the 2002 Typical and 2018 base case Base G 

emission scenarios, the secondary organic aerosol (SOA) module in CMAQ V4.5 was modified 

(SOAmods) to include additional processes related to the generation of SOA from biogenic 

emissions. In particular, three new species have been added that represent SOA products from 

biogenic emission compounds that is not included in the standard version of CMAQ V4.5 

(Morris et al., 2006c): 

• ASOC1 – SOA from biogenic sources (e.g., terpenes and isoprene) that has become 

polymerized so is no longer volatile. 

• ASOC2 – SOA from biogenic sesquiterpene and higher reactivity and higher yield 

monoterpene emissions. 

• ASOC3 – SOA from biogenic isoprene emissions. 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-11 

Thus, the species mapping for Organic Mass Carbon (OMC) and the CMAQ V4.5 SOAmods 

version of the model used in CENRAP 2018 visibility projections is as given in Table 4-2 only 

with the addition of the three new biogenic SOA species to OMC as follows: 

OMC = AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI + 

ASOC1 + ASOC2 + ASOC3 

4.3.2 Using Modeling Results to Project Changes in Visibility 

Modeling results are used in a relative fashion to project future-year visibility using relative 

response factors (RRFs). RRFs are expressed as the ratio of the modeling results for the futureyear to the results of the base year (2018/2002) and are Class I area and PM species specific. 

RRFs are applied to the Baseline Condition observed PM species to project future-year PM 

levels from which visibility can be assessed using the IMPROVE equations listed above. The 

following six steps are used to project future-year visibility for the worst 20 percent and best 20 

percent visibility days (discussion is for worst 20 percent days but also applies to best 20 percent 

days): 

1. For each Class I area and each monitored day, daily visibility is ranked using IMPROVE 

data and IMPROVE equation (either original or new IMPROVE equation) for each year 

from the five-year baseline period (2000-2004) to identify the worst 20 percent visibility 

days for each year from the five-year baseline (see Baseline Conditions discussion 

above). 

2. Use an air quality model to simulate a base year period (ideally the five-year Baseline 

period of 2000-2004, but for CENRAP just the 2002 annual period was simulated) and a 

future-year (e.g., 2018) and use the resulting information to develop Class I area-specific 

RRFs for each of the six components of light extinction in the IMPROVE equation (SO4, 

NO3, EC, OMC, Soil and CM). 

3. Multiply the RRF times the measured 24-hour PM concentration data for each day from 

the worst 20 percent days in each year from the five-year Baseline period to obtain 

projected future-year 24-hour PM concentrations for the worst 20 percent days and the 

five-year Baseline. 

4. Compute the future-year daily extinction using the IMPROVE equation and the projected 

PM concentrations for each of the worst 20 percent days in the five-year baseline from 

Step 3. 

5. For each of the worst 20 percent days within each year of the five-year baseline, convert 

the future-year daily extinction to deciview and average the daily deciview values within 

each of the five years separately to obtain five-years (or as many years with valid data in 

the 2000-2004 Baseline) of average deciview visibility for the worst 20 percent days. 

6. Average the five-years of average deciview visibility to obtain the future-year visibility 

Haze Index estimate that is the future-year estimated visibility. 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-12 

In calculating the RRFs, EPA draft guidance recommends selecting estimated PM species 

concentrations “near” the monitor by taking a spatial average of PM concentrations across a grid 

cell resolution dependent NX by NY array of cells centered on the grid containing the monitor. 

The NX x NY array of cells is grid resolution specific with EPA recommending that NX=NY=1 

for 36 km grids, NX=NY=3 for 12 km grids and NX=NY=7 for 4 km grids (EPA, 2007). For the 

CENRAP 2002 36 km modeling, just the model estimates for the grid cell containing the monitor 

was used (i.e., NX=NY=1). 

4.4 EPA Default 2018 Visibility at CENRAP and Nearby Class I areas and Comparisons to 

2018 URP Goals 

Using the EPA default visibility projection procedure described in Section 4.3 and the CENRAP 

2002 Typical Base G and 2018 Base Case Base G CMAQ modeling results, 2018 visibility 

projections were made for CENRAP and nearby Class I areas. Appendix D details the 2018 

Base G visibility projections for each Class I area in the CENRAP region using the new 

IMPROVE equation. Results for the Caney Creek (CACR), Arkansas Class I area are discussed 

in Section 4.4.1 below Displays for other CENRAP Class I areas are provided in Appendix D 

and summarized in Section 4.4.2 

4.4.1 Example 2018 Base G Visibility Projections for Caney Creek, Arkansas 

The 2018 visibility projections for the Caney Creek (CACR), Arkansas Class I area given in 

Figure D-1 in Appendix D are reproduced in Figure 4-3 and described below. 

4.4.1.1 EPA Default 2018 Visibility Projections 

The 2018 Base G visibility projection using the EPA default method (EPA, 2007a) and 

comparison with the 2018 URP point for the worst 20 percent days and the CACR Class I area is 

shown in Figure 4-3a. The 2000-2004 Baseline Conditions for CACR is 26.36 dv and the 2018 

URP point is 22.91 dv so that a 3.45 dv reduction in visibility for the worst 20 percent days is 

needed to meet the 2018 URP point. The 2018 Base G CMAQ projected visibility is 22.48 dv so 

that the modeling predicts more visibility improvements (3.88 dv reduction) than required to 

meet the 2018 URP point (3.45 dv reduction). When looking at visibility projections across 

several Class I areas, it has been useful to present the 2018 visibility projections as a percentage 

of meeting the 2018 URP point; where 100% is meeting the point, greater than 100% surpassing 

the point (i.e., below the glidepath) and less than 100% means that less visibility improvement is 

achieved than needed to meet the 2018 URP point. For 2018 Base G CMAQ modeling at 

CACR, we achieve 112% of the visibility reduction needed to meet the 2018 URP point. Note 

that meeting the 2018 URP point is not a requirement of the RHR SIPs, rather it just serves as a 

benchmark to compare progress toward Natural Conditions in 2064 and is designed to help states 

in selecting their 2018 RPGs. As clearly stated in EPA guidance “The glidepath is not a 

presumptive target, and States may establish a RPG that provides for greater, lesser, or 

equivalent improvement as that described by the glidepath” (EPA, 2007b). 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-13 

The 2018 Base G CMAQ visibility projections for the best 20 percent days and CACR is shown 

in Figure 4-3b. Recall the RHR goal for this visibility metric is no worsening of the visibility for 

the best 20 percent days. The Baseline Conditions for the best 20 percent days at CACR is 11.24 

dv. The 2018 Base G projected visibility for the best 20 percent days is 10.35 dv, which 

represents a 0.89 dv visibility improvement for the best 20 percent days at CACR and 

demonstrating no worsening in visibility for the best 20 percent days. 

Figure 4-3c displays “StackedBar Chart” plots of observed and model estimated extinction for 

each of the worst 20 percent days in 2002 and the 2002 Typical Base G CMAQ simulation and 

the average across the worst 20 percent days. This figure allows a comparison of how well the 

model is reproducing the observed extinction at CACR for the worst 20 percent days in 2002 and 

the breakdown of the PM components that are contributing to visibility impairment (more details 

on model performance were presented in Chapter 3). The 2002 worst 20 percent days at CACR 

are dominated by SO4 days (yellow), although during the winter there are also three days 

dominated by NO3 (Julian Days 80, 320 and 341). For most of the worst 20 percent days at 

CACR, the model reproduces the observed extinction reasonably well, although it does tend to 

understate SO4 on a few days and overstate NO3 on the four winter days. The observed average 

extinction across the 2002 worst 20 percent days at CACR is 150 Mm-1, compared to a modeled 

value that is 23% lower (115 Mm-1). 

Figure 4-3d displays “Boxplots” of differences in modeled extinction for the 2002 worst 20 

percent days between the 2018 Base G and 2002 Typical Base G CMAQ simulations. On most 

days SO4 is the largest component of the extinction that is estimated to be reduced at CACR on 

the worst 20 percent days. The exception to this is for the winter NO3 days where NO3 is the 

largest component of extinction that is reduced. The modeling results are not used directly in the 

visibility projections, rather they are used to develop the PM-species specific RRFs. That is, an 

important attribute in Figures 4-3c and 4-3d is the relative changes in the modeled PM species 

averaged across the worst 20 percent days that are represented by the last bar in each figure and 

provide insight into the RRFs used in the visibility projections. These results are summarized in 

Table 4-3 below. Table 4-3 compares the average extinction across the 2002 worst 20 percent 

days at CACR from the measured IMPROVE data, the modeled values and the modeled change 

in extinction between the 2018 and 2002 emissions scenarios. Although the results in Table 4-3 

are not RRFs (RRFs are based on ratios of concentrations not extinction) they do show how the 

RRFs may magnify or deflate the importance of a modeled PM species. For example, the model 

estimates that approximately 23% (26.66 Mm-1) of the visibility extinction average across the 

worst 20 percent days is due to NO3, whereas it is only 7% in the observed values (10.22 Mm-1). 

So the modeled ~40% reduction in NO3 between the 2018 and 2002 scenarios is applied to the 

smaller observed NO3 value to obtain the 2018 projected NO3 value making NO3 a smaller 

portion of the 2018 projected visibility than the 2018 modeled visibility. On the other hand, the 

modeled SO4 extinction is less than observed so that its importance in the 2018 projections is 

much greater than in the modeled 2018 SO4 values. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-14 

Table 4-3. Observed and Modeled Extinction by Species Averaged Across the Worst 20 

Percent Days in 2002 at CACR. 

2002 Average 

Observed 

W20% (Mm-1) 

2002 Average 

Modeled W20% 

(Mm-1) 

2018-2002 

Reduction 

(Mm-1) 

2018-2002 

Reduction 

(%) 

bSO4 109.50 67.90 -24.47 -36% 

bNO3 10.22 26.66 -10.90 -41% 

bOMC 19.65 16.68 -2.12 -13% 

bEC 4.38 2.32 -0.67 -29% 

bSOIL 1.43 1.04 +0.21 +20% 

bCM 4.30 0.37 -0.01 -3% 

Uniform Rate of Reasonable Progress Glide Path

Caney Creek Wilderness - 20% Data Days

26.36

25.38

22.91

20.45

17.99

15.52

13.06

11.58

22.48

0

5

10

15

20

25

30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Figure 4-3a. 2018 Visibility Projections and 2018 URP Glidepaths in Deciview for Caney Creek 

(CACR), Arkansas and Worst 20 Percent (W20%) days Using 2002/2018 Base G CMAQ 36 km 

Modeling Results. 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-15 

Uniform Rate of Reasonable Progress Glide Path

Caney Creek Wilderness - Best 20% Days

11.24 11.24 11.24 11.24 11.24 11.24 11.24 11.24

10.35

0

2

4

6

8

10

12

14

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

Haziness Index (Deciviews)

Glide Path Natural Condition (Best Days) Observation Method 1 Prediction

Figure 4-3b. 2018 Visibility Projections and 2018 URP Glidepaths in Deciview for CACR, 

Arkansas and Best 20 Percent (B20%) days Using 2002/2018 Base G CMAQ 36 m Modeling 

Results. 

Worst 20% Obs (left) vs Typ02g (right) at CACR1

0

50

100

150

200

250

300

350

400

80 128 143 155 170 173 188 191 200 203 215 218 221 239 242 248 251 254 257 260 296 320 341 _ _ Avg

Julian Day in Worst 20% group

bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-3c. Comparison of Observed (left) and 2002 Base G Modeled (right) Daily Extinction 

for Caney Creek (CACR), Arkansas and Worst 20 Percent (W20%) days in 2002. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-16 

Bext Response (base18g - typ02g) at CACR1 on Worst 20% Days

-200

-150

-100

-50

0

50

80 128 143 155 170 173 188 191 200 203 215 218 221 239 242 248 251 254 257 260 296 320 341 Avg

Julian Day

Delta Bext (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-3d. Differences in Modeled 2002 and 2018 Base G CMAQ Results (2018-2002) Daily 

Extinction for Caney Creek (CACR), Arkansas and Worst 20 Percent (W20%) Days in 2002. 

4.4.2 Summary 2018 Visibility Projections Across Class I Areas 

Figure 4-4 displays a “DotPlot” of 2018 visibility projections using the 2002 Typical and 2018 

base case Base G CMAQ 36 km modeling results. DotPlots present the 2018 visibility 

projections as a percentage of meeting the 2018 URP point. For example, at CACR the 2018 

Base G modeling achieved 112% of the visibility reduction needed to meet the 2018 URP point 

so the dot under CACR is plotted at 112%. Class I areas’ with dots above 100% surpass the 

2018 URP point (i.e., are below the glidepath), whereas Class I areas’ with dots that are under 

100% fail to meet the 2018 URP point. Figure 4-4 summarizes the 2018 visibility projections 

using the EPA default “Regular RRF” and the two alternatives where CM is assumed to be 

natural (CM RRF=1) and both CM and Soil are assumed to be natural (CM&SOIL RRF=1). 

When CM or CM&SOIL are assumed to be natural that means that we assume the same CM or 

CM&SOIL occurs in the 2018 future-year as in the 2000-2004 Baseline Conditions. For the 

CENRAP sites, the EPA default and alternative projection, assuming CM alone or CM and Soil 

are natural, techniques produced similar results. 

At the four eastern CENRAP Class I area sites close to the Mississippi River (CACR, UPBU, 

HEGL and MING), the 2018 visibility projections meet (HEGL) or surpass the 2018 URP point. 

Breton Island Class I area (BRET) comes up 6% short of meeting the 2018 URP point (i.e., 94% 

of the URP point). Wichita Mountains Class I area (WIMO) comes up approximately 40% short 

of the 2018 URP point. The two northern Class I areas (BOWA and VOYA) also come up about 

40% short of meeting the 2018 URP point (i.e., achieve 69% and 53% of the visibility 

improvement needed to meet the 2018 URP point). The two Texas Class I areas only achieve 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-17 

26% (BIBE) and 34% (GUMO) of the visibility improvement needed to meet the 2018 URP 

point for the worst 20 percent days. As discussed in more detail in Chapter 5, much of the 

difficulty for the Texas and some of the other CENRAP Class I areas in meeting the 2018 URP 

point is due to large contributions due to international transport, much of which (e.g., Mexico 

and global transport) is assumed to remain unchanged from 2002 to 2018. 

CMAQ BaseGa EPA Default Visibility Projections for CENRAP+ sites

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

BIBE1

GUMO1

WIMO1

CACR1

UPBU1

HEGL1

MING1

BRET1

VOYA2

BOWA1

MACA1

SIPS1

ISLE1

SACR1

WHIT1

WHPE1

GRSA1

ROMO1

WICA1

BADL1

THRO1

LOST1

Percent of target reduction achieved

Regular RRF

CM RRF=1

CM&SOIL RRFs=1

CENRAP non-CENRAP

Figure 4-4. 2018 Base G CMAQ Visibility Projections for CENRAP and Nearby Class I areas 

Using DotPlots that Express 2018 Visibility as a Percentage of Meeting the 2018 URP Point On 

the Deciview Linear Glidepath. 

Figure 4-5 displays the model estimated absolute change in extinction (Mm-1) averaged across 

the 2002 worst 20 percent days at Class I areas in and near the CENRAP region. The largest 

modeled reductions are in SO4 extinction. Figure 4-6 displays the percent change in the 

projected PM extinction by PM species for each CENRAP and nearby Class I area average 

across the worst 20 percent days (i.e., the relative modeled change). The four CENRAP Class I 

areas that meet the 2018 URP point (CACR, UPBU, HEGL and MING) are characterized by 

large SO4, NO3 and EC extinction reductions (30-40%) with small Soil increases. At the other 

CENRAP Class I areas, however, there are lower levels of SO4, NO3 and EC extinction 

reductions and even some NO3 increases (BIBE). At the non-CENRAP Class I areas, the two 

VISTAS Class I areas (MACA and SIPS) have large reductions in SO4 extinction (~50%), 

whereas the WRAP Class I areas SO4 extinction reductions are much smaller. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-18 

Average change in extinction components from 2002 baseline to 2018 projected

at CENRAP sites using base18g/typ02g RRFs

-60.0

-50.0

-40.0

-30.0

-20.0

-10.0

0.0

10.0

BIBE1 GUMO1 WIMO1 CACR1 UPBU1 HEGL1 MING1 BRET1 VOYA2 BOWA1

IMPROVE site

[2018 project Bext - (2000-2004 baseline Bext)] (1/Mm)

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Average change in extinction components from 2002 baseline to 2018 projected

at non-CENRAP sites using base18g/typ02g RRFs

-120.0

-100.0

-80.0

-60.0

-40.0

-20.0

0.0

20.0

MACA1 SIPS1 ISLE1 SACR1 WHIT1 WHPE1 GRSA1 ROMO1 WICA1 BADL1 THRO1 LOST1

IMPROVE site

[2018 project Bext - (2000-2004 baseline Bext)] (1/Mm)

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-5. Absolute Model Estimated Changes in Extinction (Mm-1) by PM Species for Class I 

Areas in the CENRAP region (top) and Near the CENRAP region (bottom). 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-19 

Percent change in extinction components from 2002 baseline to 2018 projected

at CENRAP sites using base18g/typ02g RRFs

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

BIBE1 GUMO1 WIMO1 CACR1 UPBU1 HEGL1 MING1 BRET1 VOYA2 BOWA1

IMPROVE site

Percent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Percent change in extinction components from 2002 baseline to 2018 projected

at non-CENRAP sites using base18g/typ02g RRFs

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

MACA1 SIPS1 ISLE1 SACR1 WHIT1 WHPE1 GRSA1 ROMO1 WICA1 BADL1 THRO1 LOST1

IMPROVE site

Percent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-6. Percent Change In Modeled Extinction by PM Species Averaged Across the 2002 

Worst 20 Percent Days for Class I areas in the CENRAP region (top) and Near the CENRAP 

region (bottom). 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-20 

4.5 2018 Visibility Projections for Base G C1 Control Scenario 

The 2018 visibility projections based on the CMAQ simulations for the 2018 Base G C1 Control 

Strategy simulations are presented in this section. The C1 Control Strategy results in reductions 

mainly in SO2 and NOx emissions from point sources in the CENRAP states. Consequently, 

PM improvements are limited to mainly SO4 and NO3 concentration reductions in the CENRAP 

states. Figure 4-7 displays the differences in CMAQ-estimated annual average SO4 and NO3 

concentrations between the 2018 Base G base case and the 2018 Base G C1 Control Strategy 

case; the differences in all other PM species (with the exception of NH4) were negligible (see: 

http://pah.cert.ucr.edu/aqm/cenrap/cmaq.shtml#base18gc1vsbase18g). Annual average SO4 

concentration reductions of over a quarter of a μg/m3 are estimated to occur in northeast Texas, 

east Oklahoma, Missouri, northeast Arkansas and up into Iowa and Illinois. There are much 

lower reductions in NO3 that cover a similar area. 

Figure 4-7. CMAQ-Estimated Reductions in Annual Average SO4 (left) and NO3 (right) 

Fine Particle Concentrations Between the 2018 Base G Base Case and 2018 Base G C1 

Control Strategy Case. 

Figure 4-8 displays the DotPlot comparisons of the 2018 visibility projections for 2018 Base G 

and 2018 Base G C1 Control Strategy emission scenarios. The additional controls in the C1 

Control Strategy are projected to result in visibility improvements for the worst 20 percent days 

at Class I areas throughout and near the CENRAP region. Sites are closer to being on the glide 

path by 10 to 30 percent. For Breton Island this makes a difference of not meeting the 2018 URP 

point in 2018 Base G (94%) to surpassing the URP point in the C1 Control Strategy (106%). 

Table 4-4 presents a tabular summary of the information presented in Figure 4-8, including the 

Baseline, 2018 URP point, and 2018 projected visibility for the Base G and C1 Control Strategy 

simulations. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc 4-21 

CMAQ BaseGc1 vs BaseG Method 1 predictions for CENRAP+ sites

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

BIBE1

GUMO1

WIMO1

CACR1

UPBU1

HEGL1

MING1

BRET1

VOYA2

BOWA1

MACA1

SIPS1

ISLE1

SACR1

WHIT1

WHPE1

GRSA1

ROMO1

WICA1

BADL1

THRO1

LOST1

Percent of target reduction achieved

BaseGc1 BaseG

CENRAP non-CENRAP

Figure 4-8. 2018 Visibility Projections as a Percentage of Meeting the 2018 URP Point 

(i.e., DotPlot) for the 2018 Base G and 2018 Base G C1 Control Strategy Emission 

Scenarios. 
September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc Table 4-4. 2000-2004 Baseline, 2018 URP Point, and Projected 2018 Visibility and Percent of Meeting the 2012018 Base G and 2018 C1 Control Strategy CMAQ Simulations. 

Class I Area Name State ID Lat. Lon. 00/04 Baseline Condit. 2018 URP Point 2018 Base G Base Case 

 (deg) (deg) (dv) (dv) (dv) (%) Badlands NP SD BADL1 43.81 -102.36 17.14 15.02 16.53 29% Big Bend NP TX BIBE1 29.33 -103.31 17.30 14.93 16.69 26% Boundary Waters Canoe Area MN BOWA1 48.06 -91.43 19.58 17.72 18.30 69% Breton LA BRET1 29.87 -88.82 25.73 22.51 22.72 94% 2Caney Creek Wilderness AR CACR1 34.41 -94.08 26.36 22.91 22.48 112% 2Great Sand Dunes NM CO GRSA1 37.77 -105.57 12.78 11.35 12.53 18% Guadalupe Mountains NP TX GUMO1 31.91 -104.85 17.19 14.74 16.35 34% Hercules-Glades Wilderness MO HEGL1 36.68 -92.9 26.75 23.14 23.06 102% 2Isle Royale NP MI ISLE1 48.01 -88.83 20.74 18.78 19.36 71% Lostwood ND LOST1 48.59 -102.46 19.57 16.87 19.27 11% Mammoth Cave NP KY MACA1 37.20 -86.15 31.37 26.64 25.60 122% 2Mingo MO MING1 37.00 -90.19 28.02 24.37 23.71 118% 2Rocky Mountain NP CO ROMO1 40.35 -105.7 13.83 12.29 13.17 43% Salt Creek NM SACR1 33.6 -104.41 18.03 15.41 17.25 30% Sipsey Wilderness AL SIPS1 34.32 -87.44 29.03 24.82 23.57 130% 2Theodore Roosevelt NP ND THRO1 46.96 -103.46 17.74 15.42 17.40 15% Upper Buffalo Wilderness AR UPBU1 36.17 -92.41 26.27 22.84 22.52 109% 2Voyageurs NP MN VOYA2 48.47 -92.8 19.27 17.58 18.37 53% White Mountain Wilderness NM WHIT1 33.48 -105.85 13.70 12.11 13.14 35% Wheeler Peak Wilderness NM WHPE1 36.57 -105.4 10.41 9.49 10.34 8% Wind Cave NP SD WICA1 43.58 -103.47 15.84 13.94 15.39 24% Wichita Mountains OK WIMO1 34.75 -98.65 23.81 20.01 21.47 61% 2
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-1 

5.0 ADDITIONAL SUPPORTING ANALYSIS 

This Chapter presents additional supporting analysis to the modeled 2018 visibility projections 

provided in Chapter 4. This supporting analysis may be used by the states in their RHR SIPs, 

along with their factor analysis, to assist in setting their 2018 RPGs for the worst 20 percent days 

and best 20 percent days. 

5.1 Comparison of CENRAP 2018 Visibility Projections with Other Groups 

2018 visibility projections for CENRAP and nearby Class I area have also been performed by the 

other RPOs. Thus, it is useful to compare the CENRAP 2018 visibility projections with those 

from the other RPOs as a quality assurance (QA) check and to foster confidence in the CENRAP 

modeling results. 

5.1.1 Comparison of CENRAP, VISTAS, MRPO and WRAP Visibility Projections 

The CENRAP 2018 Base G visibility projections were compared to the following other RPO 

visibility projections: 

• VISTAS 2018 visibility projections based on their CMAQ 12 km 2002 annual modeling 

results for the 2002 Base G and 2018 Base G2a emissions scenarios. 

• MRPO 2018 visibility projections based on their CAMx 36 km 2002 annual modeling for 

the Run 4 Scenario 1a (R4S1a) emissions scenario. 

• WRAP 2018 visibility results based on their Plan02b and Base18b CMAQ 36 km 

modeling of the 2002 calendar year. 

Figure 5-1 displays a DotPlot comparison of the four RPO visibility projections expressed as a 

percentage of achieving the 2018 URP point at CENRAP and nearby Class I areas. For the four 

CENRAP Class I areas just west of the Mississippi River in Arkansas and Missouri (CACR, 

UPBU, HEGL and MING), 2018 visibility projections are available from the CENRAP, VISTAS 

and MRPO RPOs. At HEGL, the three RPOs 2018 visibility projections are in close agreement 

with each other (estimated to achieve 99%, 101% and 95% of the 2018 URP point). The 

CENRAP and VISTAS 2018 visibility projections are also very close at the other three 

Arkansas-Missouri CENRAP Class I areas: CACR (112% and 116%), UPBU (109% and 112%) 

and MING (118% and 114%). But the MRPO 2018 visibility projections are approximately 12 

to 25 percentage points lower than the CENRAP and VISTAS projections at these three Class I 

areas, with values of 97% to 100%. The reasons why the MRPO 2018 visibility projections are 

less optimistic than CENRAP and VISTAS are unclear. However, the MRPO focused on 

visibility projections at their northern Class I areas and likely did not use the latest CENRAP 

emission estimates. In addition, the CENRAP 2018 visibility projections included BART 

controls on several sources in CENRAP states not included in the MRPO projections. Such 

BART controls are even more important in those states not covered by CAIR. 

For the Breton Island (BRET) Class I area, 2018 visibility projections are available from 

CENRAP and VISTAS. CENRAP estimates that BRET will achieve 94% of the URP point and 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-2 

VISTAS is slightly less optimistic with an 84% value. One potential contributor to this is that 

emissions from off-shore marine vessel emissions in the oil and gas production areas of the Gulf 

of Mexico are double counted in the VISTAS Base G modeling. As these emissions were 

assumed to remain unchanged between 2002 and 2018, the double counting of their emissions 

will result in stiffer RRFs than there should be and consequently less visibility benefits in 2018. 

This double counting also occurred in the CENRAP Base F modeling but was corrected in Base 

G. The double counting occurred because off-shore marine vessels were present in both the 

MMS off-shore oil/gas development inventory for the Gulf of Mexico and the VISTAS off-shore 

marine vessel inventory for the Pacific and Atlanta Oceans and the Gulf of Mexico. VISTAS 

intends to correct this double counting in their next round of modeling. 

At the two northern Minnesota Class I areas (BOWA and VOYA), the MRPO 2018 visibility 

projections (93% and 92%) exhibit more visibility improvements than CENRAP’s (69% and 

53%). This is believed to be due to higher contributions to visibility impairment from Canada in 

the CENRAP modeling. Figure 5-2 displays the CENRAP 2002 Base F total SO2 emissions and 

their differences with the 2018 Base F SO2 emissions. The SO2 emissions in Alberta Canada 

appear to be much higher and more wide spread when compared to the other provinces in 

Canada and emissions in the U.S. states. Also, there is a very large SO2 source in northern 

Manitoba (> 105

 tons/year). The Alberta SO2 emissions may be overstated in the CENRAP 

modeling, which would overstate the Canadian contribution to visibility impairment. The 

western boundary of the MRPO modeling domain was east of the Rocky Mountains so did not 

include Alberta. CENRAP confirmed that the Alberta emissions and the source in Manitoba 

were present in the emissions provided by Canada. Air parcels from Canada are generally 

associated with clean visibility conditions at the northern Minnesota Class I areas with the worst 

20 percent days generally occurring under conditions with a southerly wind component. 

However, in 2002 some of the worst 20 percent days did occur with transport out of Canada. For 

example, Figure 5-3 displays back trajectories off of the VIEWS website for two of the worst 20 

percent days at Voyageurs National Park (Julian Days 347 and 332). These back trajectories 

suggest that the potentially overstated emissions in Alberta would have an impact at VOYA 

during the worst 20 percent days in 2002. 

At the VISTAS Mammoth Cave (MACA), Kentucky Class I area, VISTAS, CENRAP and the 

MRPO estimated that 2018 visibility for the worst 20 percent days will achieve, respectively, 

122%, 123% and 102% of the 2018 URP point. The close agreement between the VISTAS 

(122%) and CENRAP (123%) 2018 visibility projections for MACA is encouraging. Why 

MRPO is 20 percentage points lower is unclear, but may be due to using earlier versions of the 

VISTAS and CENRAP emissions. The 2018 visibility projections at Sipsey (SIPS), Alabama 

estimated by VISTAS (127%) and CENRAP (130%) are also extremely close. 

Both the CENRAP and WRAP 2018 visibility projections agree that the WRAP Class I areas fail 

to achieve the 2018 URP point by a wide margin, with values achieving only ~40% or less of the 

2018 URP point. The CENRAP 2018 visibility projections agrees well with the WRAP values at 

Great Sands (GRSA), Colorado (18% vs. 15%), Badlands (BADL), South Dakota (24% vs. 

31%), Theodore Roosevelt, North Dakota (15% vs. 11%) and Lostwood (LOST), Montana (11% 

vs. 14%). There is also reasonable agreement between CENRAP and WRAP 2018 visibility 

projections at Salt Creek (SACR), New Mexico (30% vs. 12%), Rocky Mountain (ROMO), 

Colorado (43% vs. 30%), and Wind Cave (WICA), South Dakota (24% vs. 6%). There are two 

WRAP Class I areas, White Mountains (WHIT) and Wheeler Peak (WEPE), where the WRAP 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-3 

2018 visibility projections estimate that visibility will degrade for the worst 20 percent days (i.e., 

negative percent of achieving the 2018 URP point), whereas CENRAP estimates visibility 

improvements. The reasons for these differences are unclear. 

CMAQ Method 1 predictions with new IMPROVE algorithm at CENRAP+ sites Across RPOs

0%

20%

40%

60%

80%

100%

120%

140%

BIBE1

GUMO1

WIMO1

CACR1

UPBU1

HEGL1

MING1

BRET1

VOYA2

BOWA1

MACA1

SIPS1

ISLE1

SACR1

WHIT1

WHPE1

GRSA1

ROMO1

WICA1

BADL1

THRO1

LOST1

Percent of target reduction achieved

CENRAP 36k Base18g/Typ02g

VISTAS 12k 2018g2b/2002gt2a

WRAP 36k Base18b/Plan02b corrected

MwRPO 2018 R4S1a

CENRAP non-CENRAP

Figure 5-1. DotPlot comparing the CENRAP, VISTAS, MRPO and WRAP 2018 visibility 

projections expressed as a percentage of achieving the 2018 URP goal. 

Figure 5-2. 2002 Base F SO2 emissions (left) as LOG10(tons/year) and differences in 2018 

and 2002 Base F SO2 emissions (tons/year). 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-4 

Figure 5-3. Exampled back trajectories to Voyageurs National Park for two of the worst 20 

percent days from 2002: December 13, 2002 (Julian Day 347) and November 28, 2002 

(Julian Day 332). 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-5 

5.2 Extinction and PM Species Specific Visibility Projections and Comparisons to 2018 

URP Point 

It is useful to examine 2018 visibility projections by PM species to determine how each PM 

component of visibility is changing as both a diagnostic analysis of the visibility projections as 

well as whether species that are associated more with anthropogenic emissions (e.g., SO4 and 

NO3) are being reduced substantially compared to those that are less influenced by 

anthropogenic emissions (e.g., Soil and CM). However, because deciview is the natural 

logarithm of total extinction, such comparisons can not be made using the deciview scale and 

must be made using extinction. The linear glidepath from which the 2018 URP points are 

derived are based on deciview, thus to examine corresponding glidepath using extinction the 

curvature associated with the logarithmic transformation of the linear deciview glidepath to 

extinction must be accounted for in the extinction glidepath. 

5.2.1 Total Extinction Glidepaths 

Figure 5-4 displays a total extinction based glidepath for Caney Creek that is based on the EPA 

default deciview linear glidepath counterpart shown in Figure 4-3a. That is, the deciview linear 

glidepath defined by the line connecting the 26.36 dv Baseline Conditions at 2004 to the 11.58 

dv Natural Conditions in 2064. The glidepath points in 2008, 2018, 2028, etc. from the linear 

deciview glidepath (Figure 4-3a) are turned into extinction (Bext) [Bext = 10 exp(dv/10)] to 

create the curved extinction glidepath that exactly match the linear deciview glidepath points. 

Note that the 2000-2004 Baseline using the curved extinction glidepath is slightly different than 

if you just converted the deciview baseline to extinction because the logarithm relationship is 

performed before the averaging, but they are extremely close. Using the extinction curved 

glidepath, the 2018 URP point is a reduction of the Baseline 145.10 Mm-1 to 98.88 Mm-1 (a 

46.22 Mm-1 reduction). The modeled 2018 visibility projection in extinction is 97.54 Mm-1, a 

47.56 Mm-1 reduction, which achieves 103% of the reduction needed to achieve the 2018 URP 

point. Note that this compares with achieving 112% of the 2018 URP reduction point when 

using the deciview linear glidepath. The percent of achieving the 2018 URP point using the 

linear deciview and curved extinction glidepaths will rarely be the same due to the logarithmic 

relationship between the two visibility metrics and the fact that averaging within and across years 

in the deciview calculations occur after the logarithms have been applied. The greater the 

difference in extinction across the worst 20 percent days in a year and averaged across the years 

in the 2000-2004 Baseline and the greater number of years available from the 2000-2004 

Baseline may result in greater differences in the 2018 URP points using the linear deciview and 

the curved extinction glidepaths. 

Appendix F contains total extinction curved glidepaths for all the CENRAP Class I areas and 

Figure 5-5 contains a DotPlot that compares the percent of achieving the 2018 URP point at each 

CENRAP Class I area using the 2018 Base G modeling results and the linear deciview and 

curved extinction glidepaths. At most CENRAP Class I areas the ability of the 2018 modeling 

results to achieve the 2018 URP point is the same using either the deciview or extinction 

glidepaths. There are some differences at GUMO, BOWA and VOYA Class I areas which are 

due to these Class I areas having more complete data during the 2000-2004 Baseline period and 

therefore more years in the Baseline than other Class I areas as well as having variations in 

extinction across the worst 20 percent days and years (Appendix F). In any event, the closeness 

of the ability of the model to achieve the 2018 URP point using either the extinction or deciview 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-6 

glidepath verifies the validity of the extinction based glidepaths and allows for the construction 

of PM species specific glidepaths in extinction to gain insight into how each component of 

extinction is being reduced to achieve a uniform rate of progress toward natural conditions in 

2064. 

Uniform Rate of Reasonable Progress Glide Path

Caney Creek Wilderness - 20% Data Days

145.10

126.50

98.88

77.29

60.41

47.22

36.91 32.16

97.54

0

20

40

60

80

100

120

140

160

180

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

Bext (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Figure 5-4. 2018 Visibility Projections and 2018 URP Glidepaths in extinction (Mm-1) for Caney 

Creek (CACR), Arkansas and Worst 20% (W20%) days using 2002/2018 Base G CMAQ 36 km 

modeling results. 

CMAQ BaseG Method 1 predictions for CENRAP+ sites

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

BIBE1

GUMO1

WIMO1

CACR1

UPBU1

HEGL1

MING1

BRET1

VOYA2

BOWA1

MACA1

SIPS1

ISLE1

SACR1

WHIT1

WHPE1

GRSA1

ROMO1

WICA1

BADL1

THRO1

LOST1

Percent of target reduction achieved

Deciview

Extinction

CENRAP non-CENRAP

Figure 5-5. CMAQ 2018 Base G visibility projections and comparison of ability to achieve the 

2018 URP point using the EPA default deciview and alternative total extinction Glidepaths. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-7 

5.2.2 PM Species specific Glidepaths 

The VIEWS website (http://vista.cira.colostate.edu/views/) has posted PM species specific 

Natural Conditions based on the new IMPROVE equation. Using these PM species specific 

Natural Conditions and the curved extinction glidepaths we can evaluate how well visibility 

extinction achieves the 2018 URP point on a species-by-species basis. The PM species specific 

glidepaths are constructing starting with a Baseline at 2004 averaging the extinction for each PM 

species measured using the 2000-2004 IMPROVE observations and ending with the Natural 

Conditions in 2064 from the VIEWS website. Points in the glidepath for the years in between 

2004 and 2064 are constructed based on the relative differences in the 2004 Baseline and 2064 

Natural Conditions PM species extinction such that the total extinction due to all PM species at 

each interim year adds up to the same as the total extinction on the extinction-based glidepath 

(e.g., Figure 5-3). For example, for the CACR SO4 extinction glidepath the 2018 URP point is 

generated from the 2004 and 2064 SO4 extinction (BSO4) and the 2004, 2018 and 2064 total 

extinction (BTOT) as follows: 

BSO4_2018 = BSO4_2004 – [(BSO4_2004 – BSO4_2064)/ 

 (BTOT_2004- BTOT_2064)] x (BTOT_2004 – BTOT_2018) 

 = 87.05 –[(87.05 – 3.20)/(145.10 – 32.16)] x (145.10 – 98.88) 

 = 52.73 Mm-1

Note that the SO4 2018 URP point in Figure 5-5 and F-1b (52.77 Mm-1) does not exactly match the 

52.73 Mm-1 calculated due to round off error in the above calculation that only used numbers with 

precision to the nearest hundredth.

As there are larger differences between the Baseline and Natural PM species extinction for some 

species, then the rate of improvement to achieve a species specific 2018 URP point will vary 

across PM species. For example, current Baseline extinction values for Soil and CM tend to be 

closer to Natural Conditions than extinction due to SO4 and NO3. Consequently the rate of 

progress to achieve the 2018 URP point for Soil and CM will be less than for SO4 and NO3. 

Appendix F contains the PM species specific glidepaths compares them to the modeled 2018 

projections for all CENRAP Class I areas. The species specific results for the CACR Class I 

area in Figure F-1 are reproduced in Figure 5-6. The modeled rate of SO4 and NO3 extinction 

reduction is greater than the PM species specific glidepaths and both achieve the species specific 

2018 URP point by achieving 111% and 104% of the reduction needed to achieve the 2018 URP 

point. The modeled rate of extinction improvement at CACR for EC and OC is less than the 

species specific glidepath achieving only 65% and 75% of the reduction needed to achieve the 

species specific 2018 URP point. The PM species specific glidepath for Soil is flat because the 

Baseline and Natural Conditions (1.12 Mm-1) are the same. This does not mean that 

anthropogenic emissions of Soil do not contribute on worst 20 percent days at CACR. It just 

points to a mismatch between the current set of worst 20 percent days and those in 2064 under 

Natural Conditions. The worst 20 percent days in 2064 under Natural Conditions will be 

dominated by wind blown dust days when Soil and CM may be higher than during the current set 

of worst 20 percent days that are dominated by SO4, NO3 and OMC. Thus, the Soil and CM 

glidepaths tend to be flatter and in some cases may even have an upward trend for some Class I 

areas (see Appendix F). Soil is projected to increase at CACR in 2018 so does not achieve its 

species specific URP point. Little reduction in CM is also seen by 2018. As discussed 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-8 

previously, this is due in part to incompatibilities between the measured Soil and CM values at 

the IMPROVE monitor and the modeled Soil and CM species. In the model, a large component 

of the Soil and CM in the inventory is due to paved and unpaved road dust. These emissions are 

directly related to Vehicles Miles Traveled (VMT). VMT is projected to increase in future-years 

resulting in increases in road dust emissions. At the IMPROVE monitor, much of the measured 

Soil and CM is likely due to local dust events that are not simulated by the model using a 36 km 

grid resolution. Thus, the 2018 projections for Soil and CM are likely applying modeled changes 

due to road dust to local Soil and CM concentrations that in reality are likely natural and should 

remain unchanged in the future year. This is why alternative 2018 modeled projection 

approaches have been developed that assume that CM and CM and Soil are natural so remain 

unchanged in the future-year (see Section 5.5). 
 

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Uniform Rate of Reasonable Progress Glide Path

Caney Creek Wilderness - 20% Data Days

87.05

73.26

52.77

36.75

24.23

14.45

6.80 3.20

49.17

0

20

40

60

80

100

120

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

bSO4 (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Uniform Rate of Reasonable Progress Glide Path

Caney Creek Wilderness - 20% Data Days

13.78

11.69

8.58

6.14

4.24

2.76

1.60 1.05

8.36

0

2

4

6

8

10

12

14

16

18

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

bNO3 (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Uniform Rate of Reasonable Progress Glide Path

Caney Creek Wilderness - 20% Data Days

4.80

4.06

2.97

2.11

1.44

0.92

0.51 0.32

3.42

0.0 

1.0 

2.0 

3.0 

4.0 

5.0 

6.0 

7.0 

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

bEC (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Uniform Rate of Reasonable Progress Glide Path

Caney Creek Wilderness - 20% Data Days

23.44

21.56

18.78

16.60

14.90

13.57 12.53 12.04

20.42

0

5

10

15

20

25

30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

bOC (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Uniform Rate of Reasonable Progress Glide Path

Caney Creek Wilderness - 20% Data Days

1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12

1.35

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

bSOIL (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Uniform Rate of Reasonable Progress Glide Path

Caney Creek Wilderness - 20% Data Days

3.73 3.63 3.49 3.38 3.30 3.23 3.17 3.15

3.64

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

bCM (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Figure 5-6. 2018 Visibility Projections and 2018 URP Glidepaths for SO4 (top left), NO3 (top 

right), EC (middle left), OMC (middle right), Soil (bottom left) and CM (bottom right) in extinction 

(Mm-1) for Caney Creek (CACR), Arkansas and Worst 20 Percent Days using 2002/2018 Base 

G CMAQ 36 km modeling results.
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-10 

Figure 5-7 displays a DotPlot that compares the 2018 projected total and PM species specific 

extinction with the 2018 URP points. These results show that SO4 is most frequently achieving 

its 2018 URP point at those Class I areas that achieve the deciview URP point. Reductions in 

NO3 and EC also sometimes achieve their species specific URP point. 

There are some anomalies in the species specific projections and glidepaths that bear mention 

and point to areas where better estimates of emissions growth and Natural Conditions are needed 

needed. The increase in 2018 Soil projections is not an isolated incident at CACR and occurs at 

other CENRAP Class I areas. There are three CENRAP Class I areas that “achieve” the Soil 

specific 2018 URP point (HEGL, BOWA and VOYA). An examination of these glidepaths and 

visibility projections (Figures F-4f, F-5f and F-6f) reveals that the current Baseline Conditions 

Soil at these three Class I areas is actually less than the 2064 Natural Conditions so that the 

glidepath is an accent rather than reduction (Figures F-4g, F-5g and F-6g). In these three cases 

to “achieve” the 2018 URP point the modeling results must increase the projected Soil 

extinction, which is why these three Class I areas “achieve” their 2018 URP point for Soil. 

Clearly, the 2018 URP point for Soil is not very meaningful under these conditions. The current 

Baseline Conditions for OMC at BRET and BOWA is also less than the Natural Conditions 

resulting in anomalous glidepaths (Figure F-3e and F-4e). 

CMAQ BaseG Method 1 predictions for CENRAP+ sites

-200%

-180%

-160%

-140%

-120%

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

BIBE1

GUMO1

WIMO1

CACR1

UPBU1

HEGL1

MING1

BRET1

VOYA2

BOWA1

MACA1

SIPS1

ISLE1

SACR1

WHIT1

WHPE1

GRSA1

ROMO1

WICA1

BADL1

THRO1

LOST1

Percent of target reduction achieved

Bext

bSO4

bNO3

bOC

bEC

bSOIL

bCM

CENRAP non-CENRAP

Figure 5-7. Ability of total and species specific 2018 visibility projections to achieve 2018 URP 

points. 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-11 

5.3 Alternative 2018 Visibility Projection Software 

The CENRAP 2018 visibility projections were made using software developed by the CENRAP 

modeling team. PM concentrations in the 36 km grid cells containing each of the Class I area 

IMPROVE monitoring sites were extracted using the UCR Analysis Tool. These modeling data 

were then ported into Excel spreadsheets that also include the filled RHR IMPROVE database 

available from the VIEWS website along with the EPA default Natural Conditions (EPA, 

2003b). Excel macros are then used to perform the visibility projections using the EPA default 

procedures described in Chapter 4 and alternative procedures described in this Chapter. 

EPA is developing a Modeled Attainment Test Software (MATS) program that codifies the 8-

hour ozone, PM2.5 and visibility projection procedures given in EPA’s latest air quality modeling 

guidance (EPA, 2007a). The June 2007 release of the beta version of MATS is capable of 

performing 8-hour ozone and visibility projections; MATS is still under development for making 

PM2.5 projections. The June 2007 beta versions of MATS was applied to the CENRAP 2002 and 

2018 Base G 36 km CMAQ results and the resultant 2018 visibility projections were compared 

with the CENRAP values using the EPA default projection approach (see Chapter 4) at 

CENRAP and nearby Class I areas. The projected 2018 visibility estimates using the CENRAP 

and EPA MATS software are shown in Table 5-1. The biggest differences in the two 2018 

visibility projections are for the Boundary Waters (BOWA). Breton Island (BRET), and Mingo 

(MING) Class I areas where MATS produces no 2018 visibility projections. This is because 

there is insufficient capture of valid IMPROVE PM measurements within the 2000-2004 fiveyear baseline to generate three years of annual visibility estimates that is the minimum needed to 

develop the Baseline Conditions following EPA’s guidance (EPA, 2003a). For the CENRAP 

projections, data filling was used to fill out the IMPROVE measurements with sufficient data so 

that Baseline Conditions could be calculated at these three Class I areas. At 14 of the remaining 

17 Class I areas, the CENRAP and MATS 2018 visibility projections agree exactly to within a 

hundredth of a deciview. At the three sites that are different (BIBE, GUMO and ISLE) the 

difference is 0.01 dv, which is 0.06 percent or less. These differences are likely due to round off 

errors in the calculations and are not significant. These results verify the consistency with the 

CENRAP spreadsheet based and EPA MATS software for projecting future-year visibility 

estimates. 
 

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Table 5-1. Comparison of CENRAP and EPA MATS 2018 visibility projections at CENRAP and 

nearby Class I areas. 

 

2018 Visibility 

Projections 

2000-2004 

Baseline 

Conditions 

Site 

MATS 

(dv) 

CENRAP 

(dv) 

MATS 

(dv) 

CENRAP

(dv) 

BADL 16.53 16.53 17.14 17.14 

BIBE 16.70 16.69 17.30 17.30 

BOWA NA 18.30 NA 19.58 

BRET NA 22.72 NA 25.73 

CACR 22.48 22.48 26.36 26.36 

GRSA 12.53 12.53 12.78 12.78 

GUMO 16.36 16.35 17.19 17.19 

HEGL 23.06 23.06 26.75 26.75 

ISLE 19.35 19.36 20.74 20.74 

LOST 19.27 19.27 19.57 19.57 

MACA 25.60 25.60 31.37 31.37 

MING NA 23.71 NA 28.02 

ROMO 13.17 13.17 13.83 13.83 

SACR 17.25 17.25 18.03 18.03 

SIPS 23.57 23.57 29.03 29.03 

THRO 17.40 17.40 17.74 17.74 

UPBU 22.52 22.52 26.27 26.27 

VOYA 18.37 18.37 19.27 19.27 

WHIT 13.14 13.14 13.70 13.70 

WHPE 10.34 10.34 10.41 10.41 

WICA 15.39 15.39 15.84 15.84 

WIMO 21.47 21.47 23.81 23.81 

NA = Not Available 

5.4 PM Source Apportionment Modeling 

The PM Source Apportionment Technology (PSAT) was used to obtain PM source 

apportionment by geographic regions and major source category for the CENRAP 2002 and 

2018 Base E base case conditions. PSAT uses reactive tracers that operated in parallel to the 

CAMx host model using the same emissions, transport, chemical transformation and deposition 

rates as the host model to account for the contributions of user specified source regions and 

categories to PM concentrations throughout the modeling domain. Details on the formulation of 

the CAMx PSAT source apportionment can be found in the CAMx user’s guidance (ENVIRON, 

2006; www.camx.com). 

5.4.1 Definition of CENRAP 2002 and 2018 PM Source Apportionment Modeling 

PSAT calculated PM source apportionment for user defined source groups. Source groups are 

usually defined by specifying a source region map of geographic regions where source 

contributions are desired and providing source categories as input so that source group would 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-13 

consist of a geographic region plus source category (e.g., on-road mobile source emissions from 

Oklahoma). Although other source group configurations and even individual sources may be 

specified. For the CENRAP PSAT application, a source region map was used that divided up the 

modeling domain into 30 geographic source regions as shown in Figure 5-8. The 2002 and 2018 

emissions inventories were divided into six source categories. The 30 geographic source regions 

consisted of CENRAP and nearby states, with Texas divided into 3 regions, remainder of the 

western and eastern States, Gulf of Mexico, Canada and Mexico. The original intent of the 

CENRAP PSAT analysis was to obtain separate contributions due to on-road mobile, non-road 

mobile, area, natural, EGU point and non-EGU point sources. However, the CAMx emissions 

for the PSAT runs were based on the CMAQ pre-merged 3-D emission files. Since all point 

sources were contained in a single CMAQ pre-merged emissions file, then the separate source 

apportionment modeling of EGU and non-EGU point sources was not possible. The six source 

categories that were separately tracked in the PSAT PM source apportionment modeling were: 

• Elevated point sources; 

• Low-level point sources (i.e., point source emissions emitted into layer 1 of the model); 

• On-Road Mobile Sources; 

• Non-Road Mobile Sources; 

• Area Sources; and 

• Natural Sources. 

Natural Sources included biogenic VOC and NOx emissions from the BEIS3 biogenic emissions 

model, emissions from wildfires and emissions from wind blown dust due to non-agriculture 

land use types. 

PM source apportionment in PSAT is available for five families of PM tracers: (1) Sulfate; (2) 

Nitrate and Ammonium; (3) Secondary Organic Aerosols (SOA); (4) Primary PM; and (5) 

mercury. The CENRAP PSAT 2002 and 2018 applications used three of the PSAT families of 

tracers and did not use the SOA and mercury families. For SOA, the standard CAMx model 

output was used that partitions SOA into an anthropogenic (SOAA) and biogenic (SOAB) 

components. 

The PSAT results were extracted at the CENRAP and nearby Class I areas and the contributions 

for the average of the worst 20 percent and best 20 percent days were processed. A PSAT 

Visualization Tool was developed that can be used by States, Tribes and others to generate 

displays of the contributions of source regions and categories to visibility impairment for the 

average of the worst 20 percent and best 20 percent days at each CENRAP and nearby Class I 

areas. 
 

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Figure 5-8. 30 source regions used in the CENRAP 2002 and 2018 CAMx PSAT PM source 

apportionment modeling. 

5.4.2 CENRAP PSAT Visualization Tool 

The PSAT Visualization Tool allows CENRAP States, Tribes and others to visualize the 

CENRAP 2002 and 2018 PSAT modeling results and identify which source regions, categories 

and PM species are contributing to visibility impairment at Class I areas for the average of the 

worst 20 percent and best 20 percent visibility days. The Visualization Tool is currently 

available on the CENRAP website (http://www.cenrap.org) under Projects. The Tool can 

generate bar charts of source contributions at Class I areas. It can be run in a receptor oriented 

mode where it identifies the contributions of PM species and source regions and categories to 

visibility impairment on the worst and best 20 percent days. It can also be run in a source 

oriented mode to examine an individual source region’s (State’s) contribution to visibility 

impairment at downwind Class I areas on the worst and best 20% days. The original IMPROVE 

equation is used to convert the PM species concentrations to extinction. 

There are 14 air quality analysis metrics in the Tool: 

W20% Modeled Bext: The source region, source category and PM species contributions 

to the extinction (Bext) at a Class I area estimated by the model averaged across the worst 

20 percent days in 2002. 

-2736-2412-2088-1764-1440-1116 -792 -468 -144 180 504 828 1152 1476 1800 2124 2448

-2088

-1872

-1656

-1440

-1224

-1008

-792

-576

-360

-144

72

288

504

720

936

1152

1368

1584

1800
 

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W20% Projected Bext: The source region, source category and PM species contributions 

to the extinction (Bext) at a Class I area projected by the model averaged across the worst 

20 percent days in the 2000-2004 Baseline. 

W20% Modeled USAnthro: The source region, source category and PM species 

contributions to the extinction (Bext) at a Class I area for just U.S. anthropogenic 

emission source categories estimated by the model averaged across the worst 20 percent 

days in 2002. 

W20% Projected USAnthro: The source region, source category and PM species 

contributions to the extinction (Bext) at a Class I area for just U.S. anthropogenic 

emission source categories projected by the model averaged across the worst 20 percent 

days in the 2000-2004 Baseline. 

Emissions: Emissions by source region, source category and PM precursor. Precursors 

include SOx, NOx, primary organic aerosol (POA), primary elemental carbon (PEC) 

other primary fine particulate (FCRS+FPRM) and coarse mass (CCRS+CPRM). 

Emissions for four days have been extracted and implemented in the Tool. 

Control Effectiveness: Control effectiveness is defined as the PM contribution divided 

by the emissions of the primary precursor. For example the SO4 contribution divided by 

the SO2 emissions. 

Visualization Tool results are available for visibility contributions on both an absolute (Mm-1) 

and percentage basis. When looking at contributions at a given Class I area, contributions can be 

examined in terms of PM species, source regions and/or source categories. Results are available 

for both the current year (2002 modeled or 2000-2004 projected) and future year (2018). The 

“2002 W20% Project Bext” metric applies the 2002 PSAT modeled source apportionment to the 

observed 2000-2004 Baseline extinction keeping the relative contributions of source groups to 

each PM species (e.g., SO4, NO3, etc.) the same averaged across the 2002 worst 20 percent days 

but scaling their magnitudes up or down based on the ratio of the 2000-2004 Baseline to the 

2002 modeling results. Similarly, the “2018 W20% Projected” metric uses the relative 

contributions of the 2018 PSAT results from each source group and scales them according to the 

differences in the 2018 projected PM species to the 2018 modeled PM species for the average of 

the worst 20 percent days. The US Anthropogenic metrics just include source groups associated 

with U.S. man-made emissions (i.e., non-Natural source categories from states and Gulf of 

Mexico source regions) so excludes contributions from Canada and Mexico, Boundary 

Conditions, SOA from biogenic sources and the natural source category (biogenic NOx, 

wildfires and wind blown dust).

5.4.3 Source Contributions to Visibility Impairment at Class I Areas 

Appendix E displays example contributions of PM species, source regions and source categories 

to visibility impairment for the worst and best 20 percent days at the CENRAP Class I areas. 

Some of the results from Figure E-1 for the CACR Class I area are reproduced in Figures 5-9, 5-

10 and 5-11 below. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-16 

5.4.3.1 Caney Creek (CACR) Arkansas 

2002 visibility impairment for the worst 20 percent days at CACR is primarily due to SO4 from 

elevated point sources that contributes over half (66.3 Mm-1) of the total extinction of 118.8 

Mm-1 (Figure E-1a and 5-8 left). By 2018, the total extinction at CACR for the worst 20 percent 

days is reduced by approximately one third (38.5 Mm-1) which is primarily due to reductions in 

SO4 extinction from elevated point sources (from 66.3 to 37.3 Mm-1) as well as reductions in 

visibility impairment from on-road and non-road mobile sources. Even with such large 

reductions in SO4 from point sources in 2018, extinction due to elevated point sources is still the 

highest contributor to visibility impairment on the worst 20 percent days contributing over half 

(41.8 Mm-1) of the total extinction in 2018 of 80.3 Mm-1, with area sources the next most 

important source category contributing 16.0 Mm-1 (~20%). 

The geographic source apportionment for the worst 20 percent says at CACR is shown in Figures 

5-10, E-1c and E-1d. Elevated point sources from the eastern source region is the largest 

contributor in 2002 contributing almost 18 Mm-1 that is reduced by over a factor of three in 2018 

to approximately 5 Mm-1. By 2018, Arkansas is the largest contributor to extinction at CACR 

for the 20 percent worst days followed by East Texas, the large Eastern U.S. region and then 

SOA due to biogenic sources. Figures E-1e ranks the source group contributions to extinction on 

the worst 20 percent days at CACR with Elevated Point Sources from East Texas being the 

highest contributor to total extinction, similar results are seen when examining extinction at 

CACR for the worst 20 percent days due to just SO4 and NO3 (Figure E-1f). 

For the best 20 percent days at CACR (Figures 5-11, E-1g-j), SO4 is still a major contributor but 

no where near as dominate as seen for the worst 20 percent days, but elevated point is still the 

largest contributing source category Local contributions from within Arkansas contribute the 

most to the average of extinction across the best 20 percent days at CACR. 

Figure 5-9. PSAT source category by PM species contributions to the average 2000-2004 Baseline 

and 2018 projected extinction (Mm-1) for the worst 20 percent visibility days at Caney Creek 

(CACR), Arkansas. 
 

September 2007 

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Figure 5-10. PSAT source region by source category contributions to the average 2000-2004 

Baseline and 2018 projected extinction (Mm-1) for the worst 20 percent visibility days at Caney 

Creek (CACR), Arkansas.

Figure 5-11. PSAT source category by PM species contributions to the average 2000-2004 

Baseline and 2018 projected extinction (Mm-1) for the best 20 percent visibility days at Caney Creek 

(CACR), Arkansas.

5.4.3.2 Upper Buffalo (UPBU) Arkansas 

The contributions to extinction on the worst 20 percent days at UPBU (Figure E-2) is similar to 

CACR only with less contributions from East Texas and more from Missouri, Illinois and 

Indiana. By 2018, the top five highest contributing source groups to the average extinction on 

the worst 20 percent days are as follows: Arkansas Elevated Point; SOA from biogenics; 

Boundary Conditions, East Elevated Points, and Illinois Elevated Points (Figure E-2e). On the 

best 20 percent days at UPBU visibility impairment is primarily due to Arkansas and adjacent 

states Oklahoma, Missouri, and Kansas). 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-18 

5.4.3.3 Breton Island (BRET) Missouri 

Visibility impairment for the worst 20 percent days at Breton Island is primarily (69%) due to 

elevated point sources that contribute 77.7 Mm-1 out of a total of 122.2 Mm-1 (Figure E-3a). 

Although the contribution of elevated point sources is reduced substantially by 2018, they still 

contribute over half of the total extinction (101.1 Mm-1) on the worst 20 percent days at BRET 

(Figure E-3b). The top five contributing source groups to 2018 visibility impairment at BRET 

for the worst 20 percent days are: Louisiana Elevated Point Sources; Boundary Conditions; East 

Elevated Point Sources; Gulf of Mexico Area Sources and Louisiana Area Sources. Gulf of 

Mexico Area sources includes off shore shipping and oil and gas development emissions; note 

that for the PSAT simulation the off-shore marine shipping emissions were double counted 

which was corrected in the Base G emission scenarios used in the 2018 visibility projections 

discussed in Chapter 4. 

5.4.3.4 Boundary Waters (BOWA) Minnesota 

As seen for the other Class I areas, elevated point sources contribute the largest amount (47%) to 

visibility impairment at BOWA for the worst 20 percent days in 2002 (Figure E-4a). However, 

unlike many of the other Class I areas, there is little reductions (~10%) in the elevated point 

source contributions going from 2002 (29.0 Mm-1) to 2018 (26.2 Mm-1) (Figures E-4a and E-4b). 

This is because there is a slight increase in the contributions of elevated point sources in 

Minnesota from 2002 to 2018 (Figures E-4c and E-4d) that is the highest contributing source 

group (Figure E-4e). Note that the 2018 emission scenario includes growth and CAIR controls 

but no BART controls. For the best 20 percent days, the largest contributing source group by far 

is Boundary Conditions (i.e., global transport) followed by Minnesota and Canada (Figures 

E-4g-j). 

5.4.3.5 Voyageurs (VOYA) Minnesota 

Results for VOYA are similar to BOWA with Minnesota, Canada and Boundary Conditions 

contributing the most to visibility impairment on the worst and best 20 percent days (Figure E-5). 

5.4.3.6 Hercules Glade (HEGL) Missouri 

Elevated point sources contribute over half to the total extinction for the worst 20 percent days at 

HEGL in 2002 (Figures E-6a and E-6b). Going from 2002 to 2018 the contributions due to 

elevated point sources, on-road mobile and non-road mobile are reduced substantially, but the 

contributions due to the other sources remain unchanged. The largest source group contributing 

to visibility impairment on the worst 20 percents days is area sources from Missouri in both 2002 

and 2018 (Figures E-6c and E-6d). Since area emissions are not reduced much between 2002 

and 2018 and Missouri elevated point sources are mostly unchanged because the IPM model 

assumed Missouri CAIR sources would buy credits, then the Missouri contributions is only 

reduced a little going from 2002 to 2018 (from ~18 Mm-1 to ~16 Mm-1). However, the 

contributions due to the Eastern U.S., Illinois and Indiana are reduced substantially. Missouri is 

by far the largest contribution to visibility impairment at UPBU on the best 20 percent days as 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-19 

well with area sources from Missouri being the largest source category (Figures E-6h through E6j). 

5.4.3.7 Mingo (MING) Missouri 

The substantial improvements in visibility impairment at MING for the worst 20 percent days 

from 2002 (141 Mm-1) to 2018 (96 Mm-1) is primarily due to reductions in SO4 from nonMissouri elevated point sources (Figures E-7a through E-7d). Even so, with the exception of the 

top contributing Missouri area sources the largest contributing source groups to 2018 visibility 

impairment for the worst 20 percent days are still elevated point sources from several CAIR 

states (Illinois, Indiana, Missouri, East; Figure E-7e). Missouri is the largest contributor to 

visibility on the best 20 percent days followed by Boundary Conditions and Illinois (Figure 

E-7i-j). 

5.4.3.8 Wichita Mountains (WIMO) Oklahoma 

Elevated point sources are the largest contributors to visibility impairment on the worst 20 

percent days at WIMO in both 2002 and 2018 (Figures E-8a and E-8b). East Texas followed 

closely by Oklahoma are the largest contributing source regions in 2002, but by 2018 the reverse 

is true (Figures E-8c and E-8d). By 2018 the largest contributing source group to visibility 

impairment on the worst 20 percent days at WIMO is global transport (i.e., boundary conditions) 

followed by Oklahoma Area Sources and East Texas Elevated Point sources (Figure E-8e). 

Oklahoma Area Sources is the largest contributor to visibility impairment on the best 20 percent 

days at WIMO (Figures E-8g-j). 

5.4.3.9 Big Bend (BIBE) Texas 

Elevated point sources (~17 Mm-1) followed by Boundary Conditions (~12 Mm1) are the largest 

contributions to total extinction (46 Mm-1) on the worst 20 percent days at BIBE in 2002 (Figure 

E-9a). In 2018 there is very little (~2 Mm-1) reduction in the contributions of elevated point 

sources and no reductions in global transport resulting in little reductions (~7%) in visibility 

impairment on the worst 20 percent days from 2002 (46 Mm-1) to 2018 (43 Mm-1). This is due to 

the extremely large contributions of emissions from Mexico in both 2002 (Figure E-9c) and 2018 

(Figure E-9d). In fact, the four highest contributing source groups to visibility impairment at 

BIBE for the worst 20 percent days are assumed to be unchanged from 2002 to 2018: Boundary 

Conditions, Mexico Elevated Points, West Texas Natural and Mexico Natural (Figure E-9e). For 

the best 20 percent days at BIBE, West Texas, Mexico and Boundary Conditions are the highest 

three contributors to visibility impairment (Figures E-9g-j). 

5.4.3.10 Guadalupe Mountains (GUMO) Texas 

The large contribution of CM to visibility impairment at GUMO is clearly evident in the source 

apportionment modeling results (Figures E-10a-b). These sources are about evenly divided in 

the modeling between natural sources and area sources. Since these source categories are not 

reduced in the future year then there is little reduction in extinction from 2002 to 2018 (50 to 45 
 

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F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-20 

Mm-1) and what reductions there are come from Elevated Point Sources. Sources in West Texas, 

Mexico, Boundary Conditions and New Mexico are the largest contributing source regions for 

both the worst 20 percent days (Figure E-10c-e) and best 20 percent days (Figures E-10g-j). 

5.5 Alternative Visibility Projection Procedures 

In this section we analyze several alternative visibility projection procedures from the EPA’s 

default approach (EPA, 2007a) used in Chapter 4. 

5.5.1 Treatment of Coarse Mass and Soil 

As noted previously, much of the coarse mass (CM) and, to a lesser extent, Soil measured at the 

IMPROVE monitor is likely due to local wind blown dust that is natural in origin and not 

captured by the model. Consequently, even using the modeling results in a relative sense with 

the RRFs may not be appropriate for projecting CM and Soil. If CM and Soil are in fact local 

impacts due to wind blown dust from natural lands, then it would be appropriate to assume they 

are natural and remain unchanged from the 2000-2004 Baseline to 2018. This is probably 

certainly appropriate for CM because CM is primarily due to fugitive dust and it has a very short 

transport distance that is subgrid-scale to the model. In fact the model evaluation discussed in 

Chapter 3 and Appendix C clearly shows a large underprediction bias for CM that is likely due to 

local fugitive dust impacts at the IMPROVE monitor. For Soil this is less clear as fine particles 

can be transported over longer distances and is produced by anthropogenic sources, such as 

combustion and road dust, as well as natural sources. We initially performed two CM and Soil 

sensitivity tests, the first assumed CM was all natural so remains unchanged from the 2000-2004 

Baseline to 2018 (i.e., set the RRF for CM equal to 1.0). The second sensitivity test assumed 

both CM and Soil were natural so set RRFs for both of them to 1.0. A comment from an FLM 

noted that we know some of the Soil is likely anthropogenic in origin. So it was suggested to 

subtract the 2002 base case modeled Soil from the observed values for the 2002 worst 20 percent 

days and assume that the remainder (if any) was natural so hold the rest of the Soil constant in 

2018 and add to the 2018 modeled Soil values. 

The results of the CM and Soil visibility projection sensitivity analysis are shown in the DotPlot 

in Figure 5-12. The CM and Soil visibility projection sensitivity analysis has little effect on the 

2018 visibility projections at the CENRAP Class I areas. Even GUMO, which has a large CM 

and Soil component, shows very little sensitivity. This is probably because the CM at GUMO is 

likely dominated by wind blown dust that was assumed constant from 2002 to 2018 so the RRF 

calculated using the default EPA method is near 1.0 anyway. Some larger sensitivity is seen at 

several WRAP Class I areas. It is encouraging that CENRAP 2018 visibility projections are not 

sensitive to the CM and Soil components of the modeling which are highly uncertain. 
 

September 2007 

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CMAQ BaseG Method 1 predictions for CENRAP+ sites

0%

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BIBE1

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MACA1

SIPS1

ISLE1

SACR1

WHIT1

WHPE1

GRSA1

ROMO1

WICA1

BADL1

THRO1

LOST1

Percent of target reduction achieved

Regular RRF

CM RRF=1

CM&SOIL RRFs=1

RRF CM&rest(SOIL)=1

CENRAP non-CENRAP

Figure 5-12. Sensitivity of 2018 visibility projections to various methods that assume all 

CM, all CM and Soil and all CM and part of the Soil is natural. 

5.6 Alternative Model 

The CAMx model was also run for a 2002 and 2018 base case scenarios with earlier versions of 

the CENRAP emissions (Base E modified to eliminate double counting of some area fire 

emissions) than the final CMAQ 2002 Base G modeling. The CAMx 2002 and 2018 output was 

processed the same way that the CMAQ results were to generate 2018 visibility projections at 

the CENRAP and nearby Class I areas that were compared with the 2018 URP point. Figure 5-

13 summarizes the CAMx 2018 visibility projections using the new IMPROVE algorithm (NIA) 

in a DotPlot and compares them with the CMAQ 2018 Base G results (from Figure 5-12). The 

CMAQ and CAMx 2018 visibility projections are remarkably similar. The four Arkansas and 

Missouri Class I areas are projected to achieve the 2018 URP point by almost the exact same 

amount by the two models. The two Texas Class I areas are projected to come up short of 

achieving the 2018 URP point by the same amount by the two models. The largest differences 

are seen at BRET, and to a lesser extent BOWA and VOYA. At BRET the CAMx 2018 

visibility projections are much less optimistic (< 80%) in achieving the 2018 URP point than 

CMAQ (> 90%). And CMAQ is slightly less optimistic than CAMx in achieving the 2018 URP 

point for the two northern Minnesota Class I areas. The reasons for these differences are unclear 

but could be partially due to the emissions updates in the final CMAQ Base G run that included 

eliminating the double counting of off-shore marine emissions in the Gulf of Mexico that was 

present in the CAMx simulation, which makes it more difficult to get visibility improvements at 

BRET since it is influenced by sources in the Gulf. Corrections to stack parameters for Canadian 

point sources were also made for the final Base G. The general close agreement of the CAMx 

2018 visibility projections to the final CMAQ values is encouraging and good QA check. 
 

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CMAQ BaseG vs CAMx BaseE Method 1 predictions for CENRAP+ sites On Worst 20% Days

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Percent of target reduction achieved

CMAQ BaseG NIA

CAMx BaseE NIA

CENRAP non-CENRAP

Figure 5-13. Comparison of CAMx 2018 visibility projections with 2018 URP points for 

CENRAP and nearby Class I areas. 

5.7 Effects of International Transport on 2018 Visibility Projections 

As seen in the PM source apportionment modeling discussed in Section 5.4, there is significant 

contributions of international sources to visibility impairment at many CENRAP Class I areas for 

the worst 20 percent days. With the exception of Canada, where we used a year 2000 inventory 

for the 2002 base case modeling and a 2020 inventory for the 2018 inventory, international 

sources were assumed to be constant between 2002 and 2018. Thus, Class I areas that are 

heavily impacted by contributions of international transport will have a difficult time achieving 

the 2018 URP point since international sources are assumed to remain constant. The CAMx 

PSAT runs discussed previously provide a framework for quantitatively assessing the 

contributions of international transport to the visibility projections and whether reasonable 

progress toward natural conditions is being achieved in the 2018 modeling. 

There are several source regions (Figure 5-8) and source categories in the PSAT modeling that 

include international sources: 

• Mexico Anthropogenic Sources (assumed all international); 

• Canada Anthropogenic Sources (assumed all international); 

• Gulf of Mexico (assumed all U.S. sources); 

• Pacific and Atlanta Ocean (assumed all U.S. sources); and 

• Boundary Conditions (assumed half international and half natural sources). 
 

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Although it can be argued that Mexico and Canada are not truly international due to the presence 

of numerous U.S. corporations in Mexico along with free trade among the two countries, states 

and federal government have no jurisdiction to regulate industry in these two countries so they 

are considered international in these calculations. The Gulf of Mexico includes off-shore oil and 

gas production facilities, support vessels and aircraft and off-shore marine shipping. Given that 

emissions from the oil and gas production can be regulated by the U.S., then the Gulf of Mexico 

is not considered an international source. Emissions from off-shore shipping in the Pacific and 

Atlantic Oceans are also currently not regulated by the U.S. government. However, there are 

current efforts to apply some regulations to these emissions so for these calculations they were 

not assumed to be international sources. Finally, the Boundary Conditions (BCs) for the 

CENRAP modeling were generated from a 2002 simulation of the GEOS-CHEM global 

chemistry model and held constant in 2018. These BCs would include contributions from 

international sources as well as natural sources, so need to be split. For the sensitivity 

calculations discussed below we assumed that the BCs were half due to natural and half due to 

international sources. This results in international sources being defined as follows: 

 International Contribution = Mexico Anthro + Canada Anthro + 1⁄2 BCs 

Two methods were examined to see what the effects of international sources on 2018 visibility 

projections and a Class I areas ability to achieve the 2018 URP point: 

Elimination of International Contributions to 2018 Visibility Projections: In this method 

the contribution of international emissions is taken out of the 2018 visibility projections 

and examined to see whether the new visibility projection achieves the URP point. If so, 

then international sources are hindering a Class I area in achieving the 2018 URP point, 

which suggests that the 2018 URP point is not a reasonable value for an RPG. 

Visibility Projections and Glidepaths Based on Controllable Visibility Impairment: The 

second method would look at the visibility projections for just the U.S. controllable 

portion of the visibility impairment. The glidepath end point in 2064 would be to 

eliminate the U.S. man-made contributions to visibility impairment on the worst 20 

percent days. 

Note that this analysis is performed solely for providing states and others additional information 

on which Class I areas the modeling suggest are unduly influenced by International Transport. 

5.7.1 Elimination of International Contributions to 2018 Visibility Projections 

This method was also discussed in a recent technical brief prepared by the Electric Power 

Research Institute (EPRI), only in EPRI’s analysis they used results from a global chemistry 

model and VISTAS CMAQ runs with no global anthropogenic emissions (EPRI, 2007). Thus, 

before discussing our results of this analysis using PSAT, we discuss EPRI’s analysis. 
 

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5.7.1.1 EPRI’s Analysis of Effects of International Contributions 

EPRI funded Harvard University to perform annual simulations of the GEOS-Chem global 

chemistry model (http://www-as.harvard.edu/chemistry/trop/geos/) for annual simulations with 

and without non-U.S. anthropogenic emissions to determine the contributions of international 

transport to PM and visibility. The EPRI Harvard GEOS-Chem simulations were performed for 

2001. Figure 5-14 and 5-15 compare the annual average ammonium sulfate, ammonium nitrate 

organic mass carbon (OMC, also called OCM) and elemental carbon (EC) due to the GEOSChem global modeling and the CAMx PSAT source apportionment modeling. The similarity of 

the results for ammonium sulfate is remarkable (Figure 5-14). Both methods estimate that the 

annual average ammonium sulfate contribution due to international sources ranges from 0.4 to 

1.0 μg/m3

 across the Class I areas. There is less agreement between the two methods for 

ammonium nitrate due in part to a CAMx overestimation issue that is likely due in part to how 

ammonia emissions were classified as being anthropogenic or not in the no U.S. anthropogenic 

emissions simulations (Figure 5-15). Better agreement is seen between the two methods 

international contributions of OMC and EC, although CAMx estimates higher contributions than 

GEOS-Chem. 
 

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Figure 5-14. Comparison of EPRI Harvard GEOS-Chem global chemistry (top) and 

CENRAP PSAT (bottom) international source contributions to ammonium sulfate at 

Class I areas. 

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Ammonium Sulfate (μg/m3

)
 

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Figure 5-15. Comparison of EPRI Harvard GEOS-Chem global chemistry (top) and 

CENRAP PSAT (bottom) international source contributions to ammonium nitrate, organic 

carbon mass (OCM or OMC) and elemental carbon (EC) at Class I areas.

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ACADLYBRBRIGBOWAISLEBIBECACRMINGUPBUEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHEN

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Amm_NO3 OCM EC

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ACAD1LYBR1BRIG1BOWA1ISLE1BIBE1CACR1MING1UPBU1EVER1CHAS1SAMA1COHU1OKEF1ROMA1SIPS1SHRO1GRSM1LIGO1MACA1SWAN1JARI1DOSO1SHEN1

Concentration (

μg/m3

)

Ammonium Nitrate

OCM

EC
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-27 

The EPRI technical brief used the VISTAS CMAQ runs to adjust the modeled 2018 visibility 

projections to eliminate the effect of international transport and compared them to the 2018 URP 

point. For the Boundary Waters, Voyageurs, Isle Royal and Seney Class I areas the standard 

2018 visibility projections did not achieve the 2018 URP point. However, when the effect of 

transboundary pollutions was removed the 2018 URP point was essentially achieved or more 

than achieved at all four Class I areas. 

5.7.1.2 CENRAP Results From Elimination International Transport 

Because the elimination of the international sources from the 2018 visibility projections results 

in a portion of the total light extinction, then these comparisons with the 2018 URP points were 

done using extinction glidepaths and projections rather than deciview. In Section 5.2.1 we 

demonstrated that the level of achieving the 2018 URP point was almost identical at CENRAP 

Class I areas whether the linear deciview or curved extinction glidepaths were used. The PSAT 

source apportionment was used to determine the contribution to the projected extinction in 2018 

due to international sources. As noted above, international sources were assumed to be due to 

anthropogenic emissions in Mexico and Canada and half of the Boundary Conditions. 

Figure 5-16 shows the standard CAMx extinction glidepaths and 2018 visibility projections and 

the 2018 visibility projections when the contributions of international sources is eliminated. 

CACR, which achieved the 2018 URP point by 104%, achieves it by even more when 

international sources are eliminated (117%). UPBU that barely achieved the 2018 URP point by 

102% achieves it by 116% without international emissions. 

BRET comes up short of achieving the 2018 URP point when international emission are included 

(76%) as well as when they are eliminated (92%), although it is much closer (recall contributions 

of Gulf of Mexico to visibility impairment at BRET that is assumed in this analysis to be of U.S. 

origin). Eliminating international transport emissions makes of difference of meeting the 2018 

URP point without them (120%) to not meeting it with them (64%) at BOWA. Similarly at 

VOYA the standard 2018 visibility projections do not achieve the 2018 URP point (54%), 

whereas it is achieved by a far margin when international sources are eliminated (132%). 

HEGL comes up short achieving the 2018 URP point when international sources are included 

(95%), but achieves it when they are eliminated (107%). Recall the standard CAMx deciview 

visibility projections barely achieved the URP point even when international emissions are 

included (Figure 5-13). MING achieves the 2018 URP point with (106%) and without (116%) 

international sources. WIMO does not achieve the 2018 URP point when international 

contributions are eliminated. 

International sources have by far the largest effect at BIBE. Whereas the standard 2018 visibility 

projections only achieved 27% of the reductions needed to achieve the 2018 URP point, 

elimination of the international source contributions achieves 172% of the reduction needed. 

GUMO comes up short in achieving the 2018 URP point when international sources are included 

(31%), but achieves it when they are not (107%). 
 

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Uniform Rate of Reasonable Progress Glide Path

Caney Creek Wilderness - Worst 20% Days

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Uniform Rate of Reasonable Progress Glide Path

Upper Buffalo Wilderness - Worst 20% Days

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Uniform Rate of Reasonable Progress Glide Path

Breton - Worst 20% Days

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Uniform Rate of Reasonable Progress Glide Path

Boundary Waters - Worst 20% Days

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Uniform Rate of Reasonable Progress Glide Path

Voyageurs NP - Worst 20% Days

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Uniform Rate of Reasonable Progress Glide Path

Hercules-Glades Wilderness - Worst 20% Days

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Uniform Rate of Reasonable Progress Glide Path

Mingo - Worst 20% Days

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Uniform Rate of Reasonable Progress Glide Path

Wichita Mountains - Worst 20% Days

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Uniform Rate of Reasonable Progress Glide Path

Big Bend NP - Worst 20% Days

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Figure 5-16. Elimination of international sources from 2018 visibility projections and 

comparison with 2018 URP point at CENRAP Class I areas. 

5.7.2 Glidepaths Based on Controllable Extinction 

Another alternative glidepath that was examined using the CAMx PSAT source apportionment 

results was based on the U.S. anthropogenic emissions contributions to visibility impairment on 

the worst 20 percent days at the CENRAP Class I areas. The RHR strives to achieve “natural 

visibility conditions” by 2064 and defines natural conditions as conditions that would exist “in 

the absence of human caused impairment”. As shown above, anthropogenic emissions from 

international sources contribute significantly to visibility impairment at many of the CENRAP 

Class I areas making the RHR objective not practical if contributions from such sources are not 

reduced. Given that states and EPA have no jurisdiction over international sources, then we can 

not assume they will be controlled and have therefore held most of them constant at 2002 levels. 

For such Class I areas with high contributions from international sources, the comparison with 

the 2018 URP point is not very meaningful since the 2018 URP assumes such sources will be 

reduced. A more meaningful comparison would be to focus on the U.S. man-made contributions 

to visibility impairment at the Class I areas and develop a URP glidepath and 2018 URP point 

that is aimed at eliminating the U.S. anthropogenic emissions contributions to visibility 

impairment at Class I areas for the worst 20 percent days in 2064. 

The CAMx 2002 base case PSAT PM source apportionment results were processed to identify 

the portion of the 2000-2004 Baseline extinction that was due to U.S. anthropogenic emissions 

(i.e., man-made sources). The contributions of source groups that included on-road mobile, nonroad mobile, elevated point sources, low-level point sources and area sources from the PSAT 

source regions covering the U.S. states and Gulf of Mexico (Figure 5-8) were assumed to make 

up the U.S. anthropogenic contributions (i.e., excluding the Natural source category, all sources 

from the Mexico and Canada source regions and boundary conditions). Note that off-shore 

marine emissions in the Pacific and Atlantic Oceans and Gulf of Mexico were included in the 

U.S. anthropogenic emissions definition because they were in source regions associated with 

states or the Gulf of Mexico. As off-shore marine emissions may not be controllable by U.S. 

agencies and they were assumed to remain unchanged going from 2002 to 2018, then the 2018 

visibility projections for the U.S. anthropogenic component are overstated. 

The 2064 objective for the U.S. anthropogenic emissions glidepath would be no contributions on 

the worst 20 percent days. This does not mean the 2064 U.S. anthropogenic extinction objective 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-30 

is zero, rather the U.S. anthropogenic plus natural background is less than the Natural Conditions 

for the worst 20 percent days. The PSAT results were used to define the natural background 

contributions on the current worst 20 percent days which was subtracted from the EPA default 

Natural Conditions to obtain the 2064 objective for the U.S. anthropogenic emissions 

contributions. Here the PSAT derived natural background was defined as the sum of the 

contributions from the Natural source category, secondary organic aerosol from biogenic sources 

(SOAB) and half of the boundary conditions. For example, Figure 5-17 top left displays the US 

anthropogenic emissions glidepath for CACR. The PSAT natural sources contribution (=Natural 

Source Category + SOAB + 1⁄2 BC) is approximately 13 Mm-1 so that is subtracted from the 2064 

Natural Background (~32 Mm-1, see figure 5-16) to obtain a 2064 end point of ~19 Mm-1 for the 

glidepath. The 2002 PSAT results applied to the 2000-2004 Baseline extinction estimates that 

111 Mm-1 of the extinction is due to U.S. anthropogenic emissions which form the starting point 

for the glidepath. The curvature in the US anthropogenic glidepath is introduced the same way 

as for the extinction based glidepath to account for the logarithmic relationship between 

extinction and deciview. 

Figure 5-17 displays the U.S. anthropogenic emissions extinction glidepaths and comparison 

with the 2018 visibility projections for extinction due to U.S. anthropogenic emissions on the 

worst 20 percent days. As seen by the standard linear deciview glidepaths discussed in Chapter 

4, the U.S. anthropogenic emissions 2018 URP point is achieved by a wide margin at the four 

Class I areas in Arkansas and Missouri (CACR, UPBU, HRGL and MING). BRET that 

achieved 94% of the 2018 URP point obtains similar results using the U.S. anthropogenic 

emissions glidepath achieving 96% of the 2018 URP point. As discussed above, the inclusion of 

the off-shore marine emissions in the U.S. anthropogenic emissions will greatly affect the BRET 

Class I area so that actual reduction in U.S. anthropogenic emissions extinction would be greater 

and may even achieve the 2018 URP point if off-shore marine vessels were classified as not 

being part of the U.S.. 

The BOWA and VOYA northern Minnesota Class I areas achieved, respectively, 69% and 53% 

of the 2018 URP point using the standard EPA default deciview glidepaths and projection 

techniques (Figure 4-4). Using the U.S. anthropogenic glidepaths BOWA and VOYA achieve 

92% and 86% of the 2018 point, respectively (Figure 5-17). WIMO that came up approximately 

40% short of achieving the 2018 URP point using the deciview glidepath comes up under 20% 

short using the U.S. anthropogenic emissions glidepath. 

The two Texas Class I areas also come up short in achieving the 2018 URP point using the U.S. 

anthropogenic emissions glidepaths, but not as short as when the linear deciview glidepaths are 

used. BIBE increases from 26% to 67% and GUMO increases from 34% to 49%. One reason 

these two Class I areas fail to achieve the 2018 point for U.S. anthropogenic emissions is because 

of the high contributions of Soil and CM and little change in precursor emissions of these species 

between 2002 and 2018. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-31 

Uniform Rate of Reasonable Progress Glide Path

Caney Creek Wilderness - Worst 20% Days

111.38

96.84

75.02

57.89

44.36

33.52

24.48 19.38

67.20

0

20

40

60

80

100

120

140

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

BEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction 

Uniform Rate of Reasonable Progress Glide Path

Upper Buffalo Wilderness - Worst 20% Days

110.85

96.99

75.37

58.37

44.93

34.19

25.29 20.36

68.39

0

20

40

60

80

100

120

140

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

BEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction 

Uniform Rate of Reasonable Progress Glide Path

Breton - Worst 20% Days

103.43

91.55

73.24

58.61

46.91

37.59

30.16 26.48

74.69

0

20

40

60

80

100

120

140

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

BEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction 

Uniform Rate of Reasonable Progress Glide Path

Boundary Waters - Worst 20% Days

44.12

40.11

35.80

32.09

28.93

26.30 24.22 23.25

36.50

0

10

20

30

40

50

60

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

BEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction 

Uniform Rate of Reasonable Progress Glide Path

Voyageurs NP - Worst 20% Days

36.08

33.17

30.37

27.95 25.92 24.27 23.05 22.55

31.17

0

10

20

30

40

50

60

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

BEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction 

Uniform Rate of Reasonable Progress Glide Path

Hercules-Glades Wilderness - Worst 20% Days

121.44

104.95

80.70

61.90

47.26

35.76

26.46 21.42

77.22

0

20

40

60

80

100

120

140

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

BEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction 

Uniform Rate of Reasonable Progress Glide Path

Mingo - Worst 20% Days

143.07

123.51

95.29

73.48

56.61

43.54

33.34 28.26

86.80

0

20

40

60

80

100

120

140

160

180

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

BEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction 

Uniform Rate of Reasonable Progress Glide Path

Wichita Mountains - Worst 20% Days

77.98

67.77

50.99

38.13

28.21

20.41

13.88

9.96

55.49

0

10

20

30

40

50

60

70

80

90

100

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

BEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-32 

Uniform Rate of Reasonable Progress Glide Path

Big Bend NP - Worst 20% Days

15.72

14.34

12.16

10.33

8.78

7.49

6.40 5.85

13.33

0

2

4

6

8

10

12

14

16

18

20

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

BEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction 

Uniform Rate of Reasonable Progress Glide Path

Guadalupe Mountains NP - Worst 20% Days

24.20

21.58

17.68

14.36

11.50

8.98

6.67

5.28

20.98

0

3

6

9

12

15

18

21

24

27

30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

BEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction 

Figure 5-17. Glidepaths and 2018 visibility projections based on visibility due to U.S. anthropogenic 

emissions at CENRAP Class I areas. 

5.8 Use of Original IMPROVE Equation 

2018 visibility projections were also made using the CENRAP Typ02g and Base18g CMAQ 

modeling results and the original (old) IMPROVE equation. Figure 5-18 displays a DotPlot that 

compares the 2018 Base G visibility projections using the new IMPROVE algorithm (NIA) and 

the original IMPROVE algorithm (OIA). In general the new IMPROVE equation results in more 

optimistic 2018 visibility projections than the original IMPROVE equation. For the Texas and 

WRAP Class I areas, the 2018 visibility projections are nearly identical using the two IMPROVE 

equations. For the four Class I areas in Arkansas and Missouri the 2018 visibility projections 

using the new IMPROVE equation are from 7 to 21 percentage points more optimistic than the 

original IMPROVE equation. In the case of UPBU, HEGL and MING the 2018 visibility 

projections go from not achieving to achieving the 29018 URP point when switching from the 

old to new IMPROVE equation. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-33 

CMAQ BaseG Method 1 predictions for CENRAP+ sites on Worst 20% Days

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

110%

120%

130%

140%

BIBE1

GUMO1

WIMO1

CACR1

UPBU1

HEGL1

MING1

BRET1

VOYA2

BOWA1

MACA1

SIPS1

ISLE1

SACR1

WHIT1

WHPE1

GRSA1

ROMO1

WICA1

BADL1

THRO1

LOST1

Percent of target reduction achieved

BaseG NIA

BaseG OIA

CENRAP non-CENRAP

Figure 5-18. Comparison of 2018 Base G visibility projections using the New (NIA) and Old 

(OIA) IMPROVE algorithms expressed as a percentage of achieving the 2018 URP point 

visibility improvements. 

5.9 Visibility Trends 

Figure 5-19 displays trends in visibility impairment at the CENRAP Class I areas using the 

period of record of measurements at the associated IMPROVE monitor and the new IMPROVE 

equation. These trends include trends for the worst 20 percent days, the best 20 percent days and 

all IMPROVE sampled days during a year. The EPA guidance procedures were used to 

construct the worst and best 20 percent days that includes a minimum data capture requirement 

(EPA, 2003a), whereas no such minimum data capture was applied when looking at the “annual 

average” of all IMPROVE sampled days trends. So care must be taken when analyzing trends 

for the all sampled IMPROVE days trends as there could be large missing periods with high or 

low extinction that are not being account for. The WRAP Technical Support System (TSS) 

website was used to calculate the visibility trends at the CENRAP Class I areas that includes 

IMPROVE data from start of recording through 2004 and includes no data filling (see: 

http://vista.cira.colostate.edu/TSS/Default.aspx) . 

Trends in visibility at CACR has three years of data (2002-2004) for the worst and best 20 

percent days and fives years for the IMPROVE sampled days trends. Although it is hard to come 

to any conclusions regarding trends with just three years of data, there does seem to be a general 

downward trend, that is also supported by the five year trend in the IMPROVE sampled days. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-34 

A much longer trend plot is available for UPBU that includes 12 years of data for the worst and 

best 20 percent days (Figure 5-19b). Although there is a lot of a year-to-year variation in the 

visibility trends with cleaner years occurring in 1997, 2001 and 2004, there does appear to be a 

slight trend toward improved visibility at UPBU. 

There is insufficient data to calculate the worst or best 20 percent days visibility for any year at 

the BRET Class I area so only the IMPROVE sampled days trends are presented (Figure 5-19c). 

The trends at BRET are inconclusive and given the large amounts of missing data at this site it is 

difficult to interpret the results. 

There is also a lot of missing years in the worst and best 20 percent days for the BOWA Class I 

area making it difficult to interpret (Figure 5-19d). But visibility appears to be more impaired in 

the early 1990s than in more current years so improvements have been seen. VOYA has five 

years of valid data and shows worsening visibility for 2000-2003, and then improved visibility in 

2004. It is unclear whether the 2004 improved visibility is a trend or just due to variations in 

meteorology so no conclusions can be drawn. 

Although a downward trend in visibility impairment appears to be occurring at the two Missouri 

Class I areas (Figure 5-19f-g), given that there are only three years available for HEGL and lots 

of missing data for MING these trends are inconclusive. 

Three years (2002-2004) of visibility trends for the worst and best 20 percent days are available 

for WIMO (Figure 5-19h). The most impaired year from the three years for the worst 20 percent 

days is the most recent (2004). Again, the time period is too short to draw any conclusions on 

trends in visibility at WIMO. 

The two Texas Class I areas have a relatively long period of record. There is a lot of year-toyear variability in the visibility measurements that make interpreting the trends difficult. 1998 

appears to be an anomalously high visibility impairment year at BIBE and due to the much 

higher OMC extinction indicates that the year was likely impacted by smoke from fires. GUMO 

has lots of year to year variability in CM and Soil which are likely due to occurrences of impacts 

due to wind blown dust. Even taking Soil and CM out of the interpretation it is difficult to 

interpret ay trend in visibility at the two Texas Class I areas. The higher visibility impairment in 

1998 and 1999 suggests a downward trend but that may be just due to more adverse 

meteorological and natural emissions (e.g., wildfires) in these two years than any real long term 

trend. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-35 

Figure 5-19a. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at Caney Creek (CACR), Arkansas for the average of the Worst 20 Percent days (top), Best 20 

Percent days (middle) days and all IMPROVE sampling days during the period of record. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-36 

Figure 5-19b. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at Upper Buffalo (UPBU), Arkansas for the average of the Worst 20 Percent days 

(top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period 

of record. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-37 

Insufficient Data to Calculate Best 20 Percent days at BRET

Figure 5-19c. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at Breton Island (BRET), Louisiana for the average of the Worst 20 Percent days (top), Best 20 

Percent days (middle) days and all IMPROVE sampling days during the period of record. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-38 

Figure 5-19d. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) at 

Boundary Waters (BOWA), Minnesota for the average of the Worst 20 Percent days (top), Best 20 

Percent days (middle) days and all IMPROVE sampling days during the period of record. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-39 

Figure 5-19e. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at Voyageurs (VOYA), Minnesota for the average of the Worst 20 Percent days (top), Best 20 

Percent days (middle) days and all IMPROVE sampling days during the period of record. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-40 

Figure 5-19f. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at Hercules Glade (HEGL), Missouri for the average of the Worst 20 Percent days 

(top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of 

record. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-41 

Figure 5-19g. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at Mingo (MING), Missouri for the average of the Worst 20 Percent days (top), 

Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of 

record. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-42 

Figure 5-19h. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at Wichita Mountains (WIMO), Oklahoma for the average of the Worst 20 Percent 

days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the 

period of record. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-43 

Figure 5-19i. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at Big Bend (BIBE), Texas for the average of the Worst 20 Percent days (top), 

Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of 

record. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_5_AddAnal3.doc 5-44 

Figure 5-19j. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at Guadalupe Mountains (GUMO), Texas for the average of the Worst 20 Percent 

days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the 

period of record. 
 

September 2007 

F:\CENRAP_Modeling\TSD\draft#3\Chapter_6_References3.doc 6-1 

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Pollutant Air Quality Models that Simulate Regional Haze in the Southeastern United States. 

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